{
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.3-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "python37364bit02b339ca7618414dadf3fb31f69b87e2",
   "display_name": "Python 3.7.3 64-bit"
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Time</th>\n      <th>V1</th>\n      <th>V2</th>\n      <th>V3</th>\n      <th>V4</th>\n      <th>V5</th>\n      <th>V6</th>\n      <th>V7</th>\n      <th>V8</th>\n      <th>V9</th>\n      <th>...</th>\n      <th>V21</th>\n      <th>V22</th>\n      <th>V23</th>\n      <th>V24</th>\n      <th>V25</th>\n      <th>V26</th>\n      <th>V27</th>\n      <th>V28</th>\n      <th>Amount</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>-1.359807</td>\n      <td>-0.072781</td>\n      <td>2.536347</td>\n      <td>1.378155</td>\n      <td>-0.338321</td>\n      <td>0.462388</td>\n      <td>0.239599</td>\n      <td>0.098698</td>\n      <td>0.363787</td>\n      <td>...</td>\n      <td>-0.018307</td>\n      <td>0.277838</td>\n      <td>-0.110474</td>\n      <td>0.066928</td>\n      <td>0.128539</td>\n      <td>-0.189115</td>\n      <td>0.133558</td>\n      <td>-0.021053</td>\n      <td>149.62</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>1.191857</td>\n      <td>0.266151</td>\n      <td>0.166480</td>\n      <td>0.448154</td>\n      <td>0.060018</td>\n      <td>-0.082361</td>\n      <td>-0.078803</td>\n      <td>0.085102</td>\n      <td>-0.255425</td>\n      <td>...</td>\n      <td>-0.225775</td>\n      <td>-0.638672</td>\n      <td>0.101288</td>\n      <td>-0.339846</td>\n      <td>0.167170</td>\n      <td>0.125895</td>\n      <td>-0.008983</td>\n      <td>0.014724</td>\n      <td>2.69</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>-1.358354</td>\n      <td>-1.340163</td>\n      <td>1.773209</td>\n      <td>0.379780</td>\n      <td>-0.503198</td>\n      <td>1.800499</td>\n      <td>0.791461</td>\n      <td>0.247676</td>\n      <td>-1.514654</td>\n      <td>...</td>\n      <td>0.247998</td>\n      <td>0.771679</td>\n      <td>0.909412</td>\n      <td>-0.689281</td>\n      <td>-0.327642</td>\n      <td>-0.139097</td>\n      <td>-0.055353</td>\n      <td>-0.059752</td>\n      <td>378.66</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.0</td>\n      <td>-0.966272</td>\n      <td>-0.185226</td>\n      <td>1.792993</td>\n      <td>-0.863291</td>\n      <td>-0.010309</td>\n      <td>1.247203</td>\n      <td>0.237609</td>\n      <td>0.377436</td>\n      <td>-1.387024</td>\n      <td>...</td>\n      <td>-0.108300</td>\n      <td>0.005274</td>\n      <td>-0.190321</td>\n      <td>-1.175575</td>\n      <td>0.647376</td>\n      <td>-0.221929</td>\n      <td>0.062723</td>\n      <td>0.061458</td>\n      <td>123.50</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2.0</td>\n      <td>-1.158233</td>\n      <td>0.877737</td>\n      <td>1.548718</td>\n      <td>0.403034</td>\n      <td>-0.407193</td>\n      <td>0.095921</td>\n      <td>0.592941</td>\n      <td>-0.270533</td>\n      <td>0.817739</td>\n      <td>...</td>\n      <td>-0.009431</td>\n      <td>0.798278</td>\n      <td>-0.137458</td>\n      <td>0.141267</td>\n      <td>-0.206010</td>\n      <td>0.502292</td>\n      <td>0.219422</td>\n      <td>0.215153</td>\n      <td>69.99</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 31 columns</p>\n</div>",
      "text/plain": "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n\n         V8        V9  ...       V21       V22       V23       V24       V25  \\\n0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n\n        V26       V27       V28  Amount  Class  \n0 -0.189115  0.133558 -0.021053  149.62      0  \n1  0.125895 -0.008983  0.014724    2.69      0  \n2 -0.139097 -0.055353 -0.059752  378.66      0  \n3 -0.221929  0.062723  0.061458  123.50      0  \n4  0.502292  0.219422  0.215153   69.99      0  \n\n[5 rows x 31 columns]"
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('creditcard.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0, 0.5, 'Frequency')"
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_classes = pd.value_counts(data['Class'], sort = True).sort_index()\n",
    "count_classes.plot(kind = 'bar')\n",
    "plt.title('Fraud class histogram')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0    284315\n1       492\nName: Class, dtype: int64"
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>V1</th>\n      <th>V2</th>\n      <th>V3</th>\n      <th>V4</th>\n      <th>V5</th>\n      <th>V6</th>\n      <th>V7</th>\n      <th>V8</th>\n      <th>V9</th>\n      <th>V10</th>\n      <th>...</th>\n      <th>V21</th>\n      <th>V22</th>\n      <th>V23</th>\n      <th>V24</th>\n      <th>V25</th>\n      <th>V26</th>\n      <th>V27</th>\n      <th>V28</th>\n      <th>Class</th>\n      <th>normAmount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-1.359807</td>\n      <td>-0.072781</td>\n      <td>2.536347</td>\n      <td>1.378155</td>\n      <td>-0.338321</td>\n      <td>0.462388</td>\n      <td>0.239599</td>\n      <td>0.098698</td>\n      <td>0.363787</td>\n      <td>0.090794</td>\n      <td>...</td>\n      <td>-0.018307</td>\n      <td>0.277838</td>\n      <td>-0.110474</td>\n      <td>0.066928</td>\n      <td>0.128539</td>\n      <td>-0.189115</td>\n      <td>0.133558</td>\n      <td>-0.021053</td>\n      <td>0</td>\n      <td>0.244964</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.191857</td>\n      <td>0.266151</td>\n      <td>0.166480</td>\n      <td>0.448154</td>\n      <td>0.060018</td>\n      <td>-0.082361</td>\n      <td>-0.078803</td>\n      <td>0.085102</td>\n      <td>-0.255425</td>\n      <td>-0.166974</td>\n      <td>...</td>\n      <td>-0.225775</td>\n      <td>-0.638672</td>\n      <td>0.101288</td>\n      <td>-0.339846</td>\n      <td>0.167170</td>\n      <td>0.125895</td>\n      <td>-0.008983</td>\n      <td>0.014724</td>\n      <td>0</td>\n      <td>-0.342475</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-1.358354</td>\n      <td>-1.340163</td>\n      <td>1.773209</td>\n      <td>0.379780</td>\n      <td>-0.503198</td>\n      <td>1.800499</td>\n      <td>0.791461</td>\n      <td>0.247676</td>\n      <td>-1.514654</td>\n      <td>0.207643</td>\n      <td>...</td>\n      <td>0.247998</td>\n      <td>0.771679</td>\n      <td>0.909412</td>\n      <td>-0.689281</td>\n      <td>-0.327642</td>\n      <td>-0.139097</td>\n      <td>-0.055353</td>\n      <td>-0.059752</td>\n      <td>0</td>\n      <td>1.160686</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.966272</td>\n      <td>-0.185226</td>\n      <td>1.792993</td>\n      <td>-0.863291</td>\n      <td>-0.010309</td>\n      <td>1.247203</td>\n      <td>0.237609</td>\n      <td>0.377436</td>\n      <td>-1.387024</td>\n      <td>-0.054952</td>\n      <td>...</td>\n      <td>-0.108300</td>\n      <td>0.005274</td>\n      <td>-0.190321</td>\n      <td>-1.175575</td>\n      <td>0.647376</td>\n      <td>-0.221929</td>\n      <td>0.062723</td>\n      <td>0.061458</td>\n      <td>0</td>\n      <td>0.140534</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-1.158233</td>\n      <td>0.877737</td>\n      <td>1.548718</td>\n      <td>0.403034</td>\n      <td>-0.407193</td>\n      <td>0.095921</td>\n      <td>0.592941</td>\n      <td>-0.270533</td>\n      <td>0.817739</td>\n      <td>0.753074</td>\n      <td>...</td>\n      <td>-0.009431</td>\n      <td>0.798278</td>\n      <td>-0.137458</td>\n      <td>0.141267</td>\n      <td>-0.206010</td>\n      <td>0.502292</td>\n      <td>0.219422</td>\n      <td>0.215153</td>\n      <td>0</td>\n      <td>-0.073403</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 30 columns</p>\n</div>",
      "text/plain": "         V1        V2        V3        V4        V5        V6        V7  \\\n0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n\n         V8        V9       V10  ...       V21       V22       V23       V24  \\\n0  0.098698  0.363787  0.090794  ... -0.018307  0.277838 -0.110474  0.066928   \n1  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288 -0.339846   \n2  0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412 -0.689281   \n3  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321 -1.175575   \n4 -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458  0.141267   \n\n        V25       V26       V27       V28  Class  normAmount  \n0  0.128539 -0.189115  0.133558 -0.021053      0    0.244964  \n1  0.167170  0.125895 -0.008983  0.014724      0   -0.342475  \n2 -0.327642 -0.139097 -0.055353 -0.059752      0    1.160686  \n3  0.647376 -0.221929  0.062723  0.061458      0    0.140534  \n4 -0.206010  0.502292  0.219422  0.215153      0   -0.073403  \n\n[5 rows x 30 columns]"
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "data['normAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1,1))  #这里的-1是自动计算的，元素总数不变\n",
    "data = data.drop(['Time','Amount'],axis=1) #丢弃不需要的变量\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 下采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,data.columns != 'Class']\n",
    "y = data.iloc[:,data.columns == 'Class']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([   541,    623,   4920,   6108,   6329,   6331,   6334,   6336,\n         6338,   6427,   6446,   6472,   6529,   6609,   6641,   6717,\n         6719,   6734,   6774,   6820,   6870,   6882,   6899,   6903,\n         6971,   8296,   8312,   8335,   8615,   8617,   8842,   8845,\n         8972,   9035,   9179,   9252,   9487,   9509,  10204,  10484,\n        10497,  10498,  10568,  10630,  10690,  10801,  10891,  10897,\n        11343,  11710,  11841,  11880,  12070,  12108,  12261,  12369,\n        14104,  14170,  14197,  14211,  14338,  15166,  15204,  15225,\n        15451,  15476,  15506,  15539,  15566,  15736,  15751,  15781,\n        15810,  16415,  16780,  16863,  17317,  17366,  17407,  17453,\n        17480,  18466,  18472,  18773,  18809,  20198,  23308,  23422,\n        26802,  27362,  27627,  27738,  27749,  29687,  30100,  30314,\n        30384,  30398,  30442,  30473,  30496,  31002,  33276,  39183,\n        40085,  40525,  41395,  41569,  41943,  42007,  42009,  42473,\n        42528,  42549,  42590,  42609,  42635,  42674,  42696,  42700,\n        42741,  42756,  42769,  42784,  42856,  42887,  42936,  42945,\n        42958,  43061,  43160,  43204,  43428,  43624,  43681,  43773,\n        44001,  44091,  44223,  44270,  44556,  45203,  45732,  46909,\n        46918,  46998,  47802,  48094,  50211,  50537,  52466,  52521,\n        52584,  53591,  53794,  55401,  56703,  57248,  57470,  57615,\n        58422,  58761,  59539,  61787,  63421,  63634,  64329,  64411,\n        64460,  68067,  68320,  68522,  68633,  69498,  69980,  70141,\n        70589,  72757,  73784,  73857,  74496,  74507,  74794,  75511,\n        76555,  76609,  76929,  77099,  77348,  77387,  77682,  79525,\n        79536,  79835,  79874,  79883,  80760,  81186,  81609,  82400,\n        83053,  83297,  83417,  84543,  86155,  87354,  88258,  88307,\n        88876,  88897,  89190,  91671,  92777,  93424,  93486,  93788,\n        94218,  95534,  95597,  96341,  96789,  96994,  99506, 100623,\n       101509, 102441, 102442, 102443, 102444, 102445, 102446, 102782,\n       105178, 106679, 106998, 107067, 107637, 108258, 108708, 111690,\n       112840, 114271, 116139, 116404, 118308, 119714, 119781, 120505,\n       120837, 122479, 123141, 123201, 123238, 123270, 123301, 124036,\n       124087, 124115, 124176, 125342, 128479, 131272, 135718, 137705,\n       140786, 141257, 141258, 141259, 141260, 142405, 142557, 143188,\n       143333, 143334, 143335, 143336, 143728, 143731, 144104, 144108,\n       144754, 145800, 146790, 147548, 147605, 149145, 149357, 149522,\n       149577, 149587, 149600, 149869, 149874, 150601, 150644, 150647,\n       150654, 150660, 150661, 150662, 150663, 150665, 150666, 150667,\n       150668, 150669, 150677, 150678, 150679, 150680, 150684, 150687,\n       150692, 150697, 150715, 150925, 151006, 151007, 151008, 151009,\n       151011, 151103, 151196, 151462, 151519, 151730, 151807, 152019,\n       152223, 152295, 153823, 153835, 153885, 154234, 154286, 154371,\n       154454, 154587, 154633, 154668, 154670, 154676, 154684, 154693,\n       154694, 154697, 154718, 154719, 154720, 154960, 156988, 156990,\n       157585, 157868, 157871, 157918, 163149, 163586, 167184, 167305,\n       172787, 176049, 177195, 178208, 181966, 182992, 183106, 184379,\n       189587, 189701, 189878, 190368, 191074, 191267, 191359, 191544,\n       191690, 192382, 192529, 192584, 192687, 195383, 197586, 198868,\n       199896, 201098, 201601, 203324, 203328, 203700, 204064, 204079,\n       204503, 208651, 212516, 212644, 213092, 213116, 214662, 214775,\n       215132, 215953, 215984, 218442, 219025, 219892, 220725, 221018,\n       221041, 222133, 222419, 223366, 223572, 223578, 223618, 226814,\n       226877, 229712, 229730, 230076, 230476, 231978, 233258, 234574,\n       234632, 234633, 234705, 235616, 235634, 235644, 237107, 237426,\n       238222, 238366, 238466, 239499, 239501, 240222, 241254, 241445,\n       243393, 243547, 243699, 243749, 243848, 244004, 244333, 245347,\n       245556, 247673, 247995, 248296, 248971, 249167, 249239, 249607,\n       249828, 249963, 250761, 251477, 251866, 251881, 251891, 251904,\n       252124, 252774, 254344, 254395, 255403, 255556, 258403, 261056,\n       261473, 261925, 262560, 262826, 263080, 263274, 263324, 263877,\n       268375, 272521, 274382, 274475, 275992, 276071, 276864, 279863,\n       280143, 280149, 281144, 281674])"
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_records_fraud = len(data[data.Class == 1])\n",
    "fraud_indices = np.array(data[data.Class == 1].index)\n",
    "fraud_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>V1</th>\n      <th>V2</th>\n      <th>V3</th>\n      <th>V4</th>\n      <th>V5</th>\n      <th>V6</th>\n      <th>V7</th>\n      <th>V8</th>\n      <th>V9</th>\n      <th>V10</th>\n      <th>...</th>\n      <th>V20</th>\n      <th>V21</th>\n      <th>V22</th>\n      <th>V23</th>\n      <th>V24</th>\n      <th>V25</th>\n      <th>V26</th>\n      <th>V27</th>\n      <th>V28</th>\n      <th>normAmount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>541</th>\n      <td>-2.312227</td>\n      <td>1.951992</td>\n      <td>-1.609851</td>\n      <td>3.997906</td>\n      <td>-0.522188</td>\n      <td>-1.426545</td>\n      <td>-2.537387</td>\n      <td>1.391657</td>\n      <td>-2.770089</td>\n      <td>-2.772272</td>\n      <td>...</td>\n      <td>0.126911</td>\n      <td>0.517232</td>\n      <td>-0.035049</td>\n      <td>-0.465211</td>\n      <td>0.320198</td>\n      <td>0.044519</td>\n      <td>0.177840</td>\n      <td>0.261145</td>\n      <td>-0.143276</td>\n      <td>-0.353229</td>\n    </tr>\n    <tr>\n      <th>623</th>\n      <td>-3.043541</td>\n      <td>-3.157307</td>\n      <td>1.088463</td>\n      <td>2.288644</td>\n      <td>1.359805</td>\n      <td>-1.064823</td>\n      <td>0.325574</td>\n      <td>-0.067794</td>\n      <td>-0.270953</td>\n      <td>-0.838587</td>\n      <td>...</td>\n      <td>2.102339</td>\n      <td>0.661696</td>\n      <td>0.435477</td>\n      <td>1.375966</td>\n      <td>-0.293803</td>\n      <td>0.279798</td>\n      <td>-0.145362</td>\n      <td>-0.252773</td>\n      <td>0.035764</td>\n      <td>1.761758</td>\n    </tr>\n    <tr>\n      <th>4920</th>\n      <td>-2.303350</td>\n      <td>1.759247</td>\n      <td>-0.359745</td>\n      <td>2.330243</td>\n      <td>-0.821628</td>\n      <td>-0.075788</td>\n      <td>0.562320</td>\n      <td>-0.399147</td>\n      <td>-0.238253</td>\n      <td>-1.525412</td>\n      <td>...</td>\n      <td>-0.430022</td>\n      <td>-0.294166</td>\n      <td>-0.932391</td>\n      <td>0.172726</td>\n      <td>-0.087330</td>\n      <td>-0.156114</td>\n      <td>-0.542628</td>\n      <td>0.039566</td>\n      <td>-0.153029</td>\n      <td>0.606031</td>\n    </tr>\n    <tr>\n      <th>6108</th>\n      <td>-4.397974</td>\n      <td>1.358367</td>\n      <td>-2.592844</td>\n      <td>2.679787</td>\n      <td>-1.128131</td>\n      <td>-1.706536</td>\n      <td>-3.496197</td>\n      <td>-0.248778</td>\n      <td>-0.247768</td>\n      <td>-4.801637</td>\n      <td>...</td>\n      <td>-0.171608</td>\n      <td>0.573574</td>\n      <td>0.176968</td>\n      <td>-0.436207</td>\n      <td>-0.053502</td>\n      <td>0.252405</td>\n      <td>-0.657488</td>\n      <td>-0.827136</td>\n      <td>0.849573</td>\n      <td>-0.117342</td>\n    </tr>\n    <tr>\n      <th>6329</th>\n      <td>1.234235</td>\n      <td>3.019740</td>\n      <td>-4.304597</td>\n      <td>4.732795</td>\n      <td>3.624201</td>\n      <td>-1.357746</td>\n      <td>1.713445</td>\n      <td>-0.496358</td>\n      <td>-1.282858</td>\n      <td>-2.447469</td>\n      <td>...</td>\n      <td>0.009061</td>\n      <td>-0.379068</td>\n      <td>-0.704181</td>\n      <td>-0.656805</td>\n      <td>-1.632653</td>\n      <td>1.488901</td>\n      <td>0.566797</td>\n      <td>-0.010016</td>\n      <td>0.146793</td>\n      <td>-0.349231</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 29 columns</p>\n</div>",
      "text/plain": "            V1        V2        V3        V4        V5        V6        V7  \\\n541  -2.312227  1.951992 -1.609851  3.997906 -0.522188 -1.426545 -2.537387   \n623  -3.043541 -3.157307  1.088463  2.288644  1.359805 -1.064823  0.325574   \n4920 -2.303350  1.759247 -0.359745  2.330243 -0.821628 -0.075788  0.562320   \n6108 -4.397974  1.358367 -2.592844  2.679787 -1.128131 -1.706536 -3.496197   \n6329  1.234235  3.019740 -4.304597  4.732795  3.624201 -1.357746  1.713445   \n\n            V8        V9       V10  ...       V20       V21       V22  \\\n541   1.391657 -2.770089 -2.772272  ...  0.126911  0.517232 -0.035049   \n623  -0.067794 -0.270953 -0.838587  ...  2.102339  0.661696  0.435477   \n4920 -0.399147 -0.238253 -1.525412  ... -0.430022 -0.294166 -0.932391   \n6108 -0.248778 -0.247768 -4.801637  ... -0.171608  0.573574  0.176968   \n6329 -0.496358 -1.282858 -2.447469  ...  0.009061 -0.379068 -0.704181   \n\n           V23       V24       V25       V26       V27       V28  normAmount  \n541  -0.465211  0.320198  0.044519  0.177840  0.261145 -0.143276   -0.353229  \n623   1.375966 -0.293803  0.279798 -0.145362 -0.252773  0.035764    1.761758  \n4920  0.172726 -0.087330 -0.156114 -0.542628  0.039566 -0.153029    0.606031  \n6108 -0.436207 -0.053502  0.252405 -0.657488 -0.827136  0.849573   -0.117342  \n6329 -0.656805 -1.632653  1.488901  0.566797 -0.010016  0.146793   -0.349231  \n\n[5 rows x 29 columns]"
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#随机进行选择\n",
    "normal_indices = data[data.Class == 0].index\n",
    "\n",
    "# 随机选了500左右的样本\n",
    "random_normal_indices = np.random.choice(normal_indices,number_records_fraud,replace=False)\n",
    "random_normal_indices = np.array(random_normal_indices)\n",
    "\n",
    "# 整合两个数据\n",
    "under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])\n",
    "\n",
    "under_sample_data = data.iloc[under_sample_indices,:]\n",
    "X_undersample = under_sample_data.iloc[:,under_sample_data.columns != 'Class']\n",
    "y_undersample = under_sample_data.iloc[:,under_sample_data.columns == 'Class']\n",
    "\n",
    "X_undersample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>541</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>623</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4920</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6108</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6329</th>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "      Class\n541       1\n623       1\n4920      1\n6108      1\n6329      1"
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_undersample.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Whole dataset 训练集，验证集\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.3,random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 测试集\n",
    "X_train_undersample,X_test_undersample,y_train_undersample,y_test_undersample = train_test_split(X_undersample,y_undersample,test_size=0.3,random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Recall= TP/(TP+FN) 召回率或者叫查全率\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold, cross_val_score \n",
    "#KFold做几倍的交叉验证，cross_val_score评估结果\n",
    "from sklearn.metrics import confusion_matrix,recall_score,classification_report\n",
    "# confusion_matrix混淆矩阵\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printing_Kfold_scores(x_train_data,y_train_data):\n",
    "    kf = KFold(5, shuffle=False)  # 把原始训练集切分为5份\n",
    "\n",
    "    #Differnt C parameters 正则化惩罚项\n",
    "    c_param_range = [0.01,0.1,1,10,100]\n",
    "\n",
    "    results_table = pd.DataFrame(index = range(len(c_param_range), 2), columns=['C_parameter', 'Mean recall score'])\n",
    "    results_table['C_parameter'] = c_param_range\n",
    "\n",
    "    # the kfold will give 2 lists: train_indices = indices[0], test_indices = indices[1]\n",
    "    j = 0\n",
    "    for c_param in c_param_range:\n",
    "        print('--------------------------------------')\n",
    "        print('C parameter: ', c_param)\n",
    "        print('--------------------------------------')\n",
    "\n",
    "        recall_accs = []\n",
    "        # iteration 重复项, indices指标\n",
    "        # enumerate 枚举方法\n",
    "        for iteration, indices in enumerate(kf.split(y_train_data),start=1):\n",
    "            # Call the logistiv regression model with a certain C parameter\n",
    "            lr = LogisticRegression(C = c_param)\n",
    "\n",
    "            # Use the training data to fit the model. In this case, we use the portion of the fole to train the model\n",
    "            # with indices[0] We then predict on the portion asigned as the 'test cross validation' with the indices\n",
    "            lr.fit(x_train_data.iloc[indices[0],:], y_train_data.iloc[indices[0],:].values.ravel())\n",
    "\n",
    "            #Predict values using the test indices in the taining data\n",
    "            y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values)\n",
    "\n",
    "            #Calculate the recall score and append it to a list for recall scores representing the current c_paramer\n",
    "            recall_acc = recall_score(y_train_data.iloc[indices[1],:].values,y_pred_undersample)\n",
    "            recall_accs.append(recall_acc)\n",
    "            print('Iteration ',iteration,' : recall score = ', recall_acc)\n",
    "        # The mean value of those recall scores is the metric we want to save and get hold of.\n",
    "        results_table.iat[j,1] = np.mean(recall_accs)\n",
    "        j += 1\n",
    "        print('')\n",
    "        print('Mean recall score ', np.mean(recall_accs))\n",
    "        print('results_table',results_table)\n",
    "    # results_table.loc[]['C_parameter']]\n",
    "    # Finally, we can check which C parameter is the best amongst the chosen.\n",
    "    best_c = results_table['Mean recall score'].max()\n",
    "    print('*********************************************************************************')\n",
    "    print('Best model to choose from cross validation is with C parameter = ', best_c)\n",
    "    print('*********************************************************************************')\n",
    "    return best_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "--------------------------------------\nC parameter:  0.01\n--------------------------------------\nIteration  1  : recall score =  0.821917808219178\nIteration  2  : recall score =  0.863013698630137\nIteration  3  : recall score =  0.9152542372881356\nIteration  4  : recall score =  0.9324324324324325\nIteration  5  : recall score =  0.8939393939393939\n\nMean recall score  0.8853115141018554\nresults_table    C_parameter Mean recall score\n0         0.01          0.885312\n1         0.10               NaN\n2         1.00               NaN\n3        10.00               NaN\n4       100.00               NaN\n--------------------------------------\nC parameter:  0.1\n--------------------------------------\nIteration  1  : recall score =  0.863013698630137\nIteration  2  : recall score =  0.8767123287671232\nIteration  3  : recall score =  0.9661016949152542\nIteration  4  : recall score =  0.9459459459459459\nIteration  5  : recall score =  0.9090909090909091\n\nMean recall score  0.9121729154698739\nresults_table    C_parameter Mean recall score\n0         0.01          0.885312\n1         0.10          0.912173\n2         1.00               NaN\n3        10.00               NaN\n4       100.00               NaN\n--------------------------------------\nC parameter:  1\n--------------------------------------\nIteration  1  : recall score =  0.863013698630137\nIteration  2  : recall score =  0.8767123287671232\nIteration  3  : recall score =  0.9830508474576272\nIteration  4  : recall score =  0.9459459459459459\nIteration  5  : recall score =  0.9090909090909091\n\nMean recall score  0.9155627459783485\nresults_table    C_parameter Mean recall score\n0         0.01          0.885312\n1         0.10          0.912173\n2         1.00          0.915563\n3        10.00               NaN\n4       100.00               NaN\n--------------------------------------\nC parameter:  10\n--------------------------------------\nIteration  1  : recall score =  0.8767123287671232\nIteration  2  : recall score =  0.8767123287671232\nIteration  3  : recall score =  0.9830508474576272\nIteration  4  : recall score =  0.9459459459459459\nIteration  5  : recall score =  0.9090909090909091\n\nMean recall score  0.9183024720057457\nresults_table    C_parameter Mean recall score\n0         0.01          0.885312\n1         0.10          0.912173\n2         1.00          0.915563\n3        10.00          0.918302\n4       100.00               NaN\n--------------------------------------\nC parameter:  100\n--------------------------------------\nIteration  1  : recall score =  0.8767123287671232\nIteration  2  : recall score =  0.8767123287671232\nIteration  3  : recall score =  0.9830508474576272\nIteration  4  : recall score =  0.9459459459459459\nIteration  5  : recall score =  0.9090909090909091\n\nMean recall score  0.9183024720057457\nresults_table    C_parameter Mean recall score\n0         0.01          0.885312\n1         0.10          0.912173\n2         1.00          0.915563\n3        10.00          0.918302\n4       100.00          0.918302\n*********************************************************************************\nBest model to choose from cross validation is with C parameter =  0.9183024720057457\n*********************************************************************************\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
    }
   ],
   "source": [
    "best_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘画图像\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=0)\n",
    "    plt.yticks(tick_marks, classes)\n",
    " \n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    " \n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.9183024720057457"
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_cnf(C_arg,x_train_data,y_train_data,x_test_data,y_test_data,precision=2):\n",
    "\n",
    "    #LogisticRegression\n",
    "    lr = LogisticRegression(C = C_arg)\n",
    "    lr.fit(x_train_data, y_train_data)\n",
    "    y_pred = lr.predict(x_test_data.values)\n",
    "\n",
    "    # Compute confusion matrix\n",
    "    cnf_matrix = confusion_matrix(y_test_data, y_pred)\n",
    "    np.set_printoptions(precision)\n",
    "    print(\"Recall metric in the testing dataset: \", cnf_matrix[1, 1] / (cnf_matrix[1, 0] + cnf_matrix[1, 1]))\n",
    "    # Plot non-normalized confusion matrix\n",
    "    class_names = [0, 1]\n",
    "    plt.figure()\n",
    "    plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Recall metric in the testing dataset:  0.9387755102040817\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 2 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘画下采样数据的混淆矩阵\n",
    "show_cnf(\n",
    "    best_c,\n",
    "    X_train_undersample , \n",
    "    y_train_undersample.values.ravel(),\n",
    "    X_test_undersample,\n",
    "    y_test_undersample\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Recall metric in the testing dataset:  0.9319727891156463\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 2 Axes>"
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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   ],
   "source": [
    "# 绘画真实数据的混淆矩阵\n",
    "show_cnf(\n",
    "    best_c,\n",
    "    X_train_undersample , \n",
    "    y_train_undersample.values.ravel(),\n",
    "    X_test,\n",
    "    y_test,\n",
    "    2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "--------------------------------------\nC parameter:  0.01\n--------------------------------------\nIteration  1  : recall score =  0.5373134328358209\nIteration  2  : recall score =  0.6164383561643836\nIteration  3  : recall score =  0.6666666666666666\nIteration  4  : recall score =  0.6\nIteration  5  : recall score =  0.5\n\nMean recall score  0.5840836911333742\nresults_table    C_parameter Mean recall score\n0         0.01          0.584084\n1         0.10               NaN\n2         1.00               NaN\n3        10.00               NaN\n4       100.00               NaN\n--------------------------------------\nC parameter:  0.1\n--------------------------------------\nIteration  1  : recall score =  0.5522388059701493\nIteration  2  : recall score =  0.6164383561643836\nIteration  3  : recall score =  0.7166666666666667\nIteration  4  : recall score =  0.6153846153846154\nIteration  5  : recall score =  0.5625\n\nMean recall score  0.612645688837163\nresults_table    C_parameter Mean recall score\n0         0.01          0.584084\n1         0.10          0.612646\n2         1.00               NaN\n3        10.00               NaN\n4       100.00               NaN\n--------------------------------------\nC parameter:  1\n--------------------------------------\nIteration  1  : recall score =  0.5522388059701493\nIteration  2  : recall score =  0.6164383561643836\nIteration  3  : recall score =  0.7333333333333333\nIteration  4  : recall score =  0.6153846153846154\nIteration  5  : recall score =  0.575\n\nMean recall score  0.6184790221704963\nresults_table    C_parameter Mean recall score\n0         0.01          0.584084\n1         0.10          0.612646\n2         1.00          0.618479\n3        10.00               NaN\n4       100.00               NaN\n--------------------------------------\nC parameter:  10\n--------------------------------------\nIteration  1  : recall score =  0.5522388059701493\nIteration  2  : recall score =  0.6164383561643836\nIteration  3  : recall score =  0.7333333333333333\nIteration  4  : recall score =  0.6153846153846154\nIteration  5  : recall score =  0.575\n\nMean recall score  0.6184790221704963\nresults_table    C_parameter Mean recall score\n0         0.01          0.584084\n1         0.10          0.612646\n2         1.00          0.618479\n3        10.00          0.618479\n4       100.00               NaN\n--------------------------------------\nC parameter:  100\n--------------------------------------\nIteration  1  : recall score =  0.5522388059701493\nIteration  2  : recall score =  0.6164383561643836\nIteration  3  : recall score =  0.7333333333333333\nIteration  4  : recall score =  0.6153846153846154\nIteration  5  : recall score =  0.575\n\nMean recall score  0.6184790221704963\nresults_table    C_parameter Mean recall score\n0         0.01          0.584084\n1         0.10          0.612646\n2         1.00          0.618479\n3        10.00          0.618479\n4       100.00          0.618479\n*********************************************************************************\nBest model to choose from cross validation is with C parameter =  0.6184790221704963\n*********************************************************************************\nRecall metric in the testing dataset:  0.6190476190476191\n"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 2 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 拿到数据不处理就开始给机器学习\n",
    "best_c = printing_Kfold_scores(X_train, y_train)\n",
    "show_cnf(\n",
    "    best_c,\n",
    "    X_train, \n",
    "    y_train.values.ravel(),\n",
    "    X_test,\n",
    "    y_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 阈值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Recall metric in the testing dataset:  0.9863945578231292\nRecall metric in the testing dataset:  0.9523809523809523\nRecall metric in the testing dataset:  0.9319727891156463\nRecall metric in the testing dataset:  0.9115646258503401\nRecall metric in the testing dataset:  0.8707482993197279\nRecall metric in the testing dataset:  0.8571428571428571\nRecall metric in the testing dataset:  0.8503401360544217\nRecall metric in the testing dataset:  0.8163265306122449\nRecall metric in the testing dataset:  0.7687074829931972\n"
    },
    {
     "data": {
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IgSmvIuEILa91WRcgkjf1t8NsaRGR7KWVVzO71szmmNlLJevWNbMHzOyN+M914vVmZpeb2TQze8HMtq/iRxRpM5LmNQ/fsWogi5SKu4CSLCKSsXTzOh4YVbbuNOBBdx8EPBg/B9gXGBQvY4HfpfFxRNq0CvKah+9YDbEo8/Wt12fUFr1wh3fmLeHX/3yLLdbvyvd23pD2dcYbH37CJQ+9xQrPutJ8WLLoY2751WnMevt1zIxvnfYr1ttwY8af80PmzZ7Buuv34zvnX8GaXdfOutREonkac5BMadRVZx7KvrtszofzF7PDmN8AcPbYvTlg9y1ZscL5cP5ixv7sVmbN/RiA3bbfmIt/dCAd2tfx0YIl7P3fV2VZfqZOOP67/P3eu+nZaz3+Pfl5AH5+/tnce9ed1NXV0bNXL64cdy19+myQcaXJpZlXd3/EzAaWrT4I2CN+fB3wL+DUeP317u7AJDPrbmZ93H1WKsW0IVedM4Z9dx/Kh/MWscOhv2jw2klH7smFP/4G/b5yKh8t+ISTjxrJ4fvtCED7dnUM2Wh9+u95GvM/XpJF6bkyffp0vvudo5gz5wPMjGOPG8sJJ56UdVkVCe37VWeQS/RYqwMHbd2bH972EsdPfJE6M74yqCc/Gbkxv/z7NI6f+CJzFn3GV4f0yrrU3Pjz5eez+U5f5qyb/sGpf7yb3gM25R83XsVmw3bmf295iM2G7cwDN4Z0ciW97h912abvhrsnc9DJ1zRYd8mNDzP825cw4qhLuffxVzj92L0AWLtLJy776dc59KfjGfat3zDmzBuyKDk3vvXto7jtr3c3WPfDH/2Ex556lkcmPcM+++7Pxb+8IKPqWquivPY0s8kly9gEO+hd0uidDfSOH/cFppdsNyNeJ2VuuHMSB/3gylXW9+vdnZEjNue9WfNWrrvk+gcZMfpCRoy+kLP/7w4efeYNNY5j7du358KLfs2zL7zMw49N4vdXXckrL7+cdVkVSp7XPDSk1UAu067OWKN9HXUGHdvX8enyL1j2hTNz4acATJnxMbtsvG7GVebD0sUfM+35p/jSAYcB0L7DGqzZtRsvPvYAw0cdAsDwUYfw4qMPZFlmxdRlm1+PP/c288q+MBct+Wzl4zU7rYETde8cvs92/O1fLzH9gwUAfDj/k9oVmkM777o766zb8N+ubt26rXy85JNPcvGlVKkK8jrX3XcoWcZVsp/4bLH6Div0+JQ3mbdw1UbuRT85hDMv+yvRr3VVh43agVvve6ba5QWjT58+bLd9dN6ka9euDBmyOe+/PzPjqiqnIRaB+uiTZdz+3CxuOGo7Plu+ginTF/LItHkc96UNGdRrLd748BN222RdenVZI+tSc+GjWTPo0n1dbvrFKcx88xX6bzaUQ046m0Xz57J2z/UA6NajF4vmz8240goY1NWpyzY05x6/D2P2HcbCxZ8y6ge/B2BQ/560b9+O+3/7fbqs2ZErJz7GzfdOybjS/Lng3LOYcPONdOu2Nnfc+4+sy6lMinltwgf1OTSzPsCceP1MoH/Jdv3idZLAAXtsxftzFvDi643/yjp36sBXd96cky+8tcaVheHdd97hueeeZcfhO2VdSmVSzquZXQscAMxx96HxunWBicBA4B3gMHefb9HR/2XAfsAS4Bh3b/YLoapnkM1slJm9Fncbn9byT2SrS8d2fGngOhxzw3OMue5ZOrWvY8/NenDh36fx/V025LJDtmTp51+wookj3qJZ8cVyZrw+lV0PHsOp195Fx85r8o+bGo7xjP5O5uBQMKH6MVJF7LINLa+lzr3qfgYd9Asm3P8sx39zZyAaw7j9kL58/cfXcuBJV3P6sXuxaf+eGVeaP2edewEvvf4Ohx5+BH/4/apd4XlWYV5b4w7g6Pjx0cDfStYfFQ+NGgEszOJgNsTMdu7UgVOO3Yfzf3d3k9vsv/tWPPHcWxpe0YjFixdzxGGHcPGvL23QAxSCSvKaMLPjqWIvbdUayGbWDrgyLmoL4Agz26Ja+0vDdv3W5oNFn7Hw0+V8scJ5/O35bL5+V175YDE/+esrnPSnqbw4axEzF3yadam50L1XH7r3Wp+BW24LwLZ7jGL6ay/RdZ2eLJwbnWhZOHcOXdfpkWWZFStil22IeW3MxPuf5eCvbAXAzDkLeWDS6yz5dBkfLVzCY8++xdaD+mRcYX4dOvpb3PnXv2RdRsXS6q41s1uAJ4DBZjbDzI4DLgS+amZvAHvFzwHuAd4CpgF/AP67Ch+tpXqDzOzG/XoxoG8Pnpp4Oq/efR591+vOEzefSu8eXVduc+g+w7hNwytWsWzZMo447BAOP2IMB3/9G1mX0yppDrFw90eAeWWrDyLqnSX+8+CS9dd7ZBLQPe4ValI1zyAPB6a5+1vu/jkwIS4wt+Ys+owhvbvQsX30a9m2bzemz1/K2p2jkSgd6oxDt+vD3VPnNPc2hdGtRy+6r9eHD957C4DXnvk36w8cxNBd9uKp+/4EwFP3/Ymtdv1qlmVWrMpnpD6oD2XOumyDy2u9TUrOCh+w+xa8/m70K73z0ZfZeZuBtGtXR+eOHdhxyw159R1lt9Sb095Y+fieu+5g0ODBGVbTOmnl1d2PcPc+7t7B3fu5+zXu/pG7j3T3Qe6+l7vPi7d1d/+Bu2/i7lu5++Sqf9BVBZnZqdPeZ8DI0xmy/zkM2f8cZs5ZwJe+9Ss++GgRAN26dGLXYZty579eyLjSfHF3jv/ecQwesjknnfzjrMtptQrPIGfaS1vNMciNFZPrATOvzfmER9+cxxWHDuWLFc6bc5dw79Q5HL1TP4YPXIc64K6pc3h+5sdZl5ob3/zRuVx//o/4YtkyemywIWPOuAhfsYI/nn0Ck+6+lXV69+U751+RdZkVqfLFAfVdtheyapftCWY2gSgnte6yDSKv153/LXbbfmN6dl+LaXecwc/+8ACjdh7CoA17scKd92bP58Rf/RmA196ZwwOTXufpG09mxQpn/B1P8fJbH2T8CbLz3aPH8PijD/PRR3PZctAATjvrHB64/16mvf46dXV19N9wQ359+W+zLrNiebiYJyNhZPaXx7DbsEH07N6Faff9jJ9ddQ/X/fWJJrc/8Cvb8OCkV1ny6ec1rDL//v3449x80w0MHboVOw2Lem3Pu+AXjNp3v4wrq0yFeZ3r7ju0dl/u7mbW6l7azC/Si48IxgJ0Wnf9jKuBG5+eyY1PNzxxd/UT07n6ielN/ESx9Ru0BT+9+o5V1p9w2U0ZVJMCS2+exrjLdg+io+AZwDlEDeNb4+7bd4HD4s3vIbp4YBrRBQTfSaWIlJXmlTVqP/7t6LNvXmXddXc+3eT2l9z0MJfc9HA1SwrG1detmskjjz42g0pSlGJe26IGee3QJZMajj59fLOvD9n/nAbPb7zzSW6888kqVhSmXXbdlaXLcjEir/Vqk9fULqytZgM5UTHxuM1xAGsP2Dzw//sSuugignTey92PaOKlkY1s68AP0tlzq1Sc17ou6yuvkqk08xqgFjPbIK9rrqe8SqZqlNfUemmr2UB+GhhkZhsRhXY08K0q7k8kBVbtaaPySnmVABU2r6DMSnDSzWu1e2mr1kB29+VmdgJwP9AOuNbdp1ZrfyJpKWKXrfIqoSpiXkGZlTClmddq99JWdQyyu99D1GoXCUNO7uCTBeVVglPgvIIyK4EJLK+ZX6Qnkif1E5mLSP4pryLhCC2vaiCLlAkpwCJFp7yKhCOkvKqBLFImoPyKFJ7yKhKOkPKqBrJImZCOcEWKTnkVCUdIeVUDWaSEWaGnjRIJivIqEo7Q8qoGskiZgA5wRQpPeRUJR0h5VQNZpExdSAkWKTjlVSQcIeVVDWSRMgHlV6TwlFeRcISUVzWQRUqYhXURgUiRKa8i4Qgtr002kM2sW3M/6O4fp1+OSPYCuoagAWVWikh5FQlHSHlt7gzyVMCJbn5Sr/65AxtWsS6RzIR0lW0ZZVYKR3kVCUdIeW2ygezu/WtZiEgeGGCEE+BSyqwUjfIqEo7Q8lqXZCMzG21mZ8SP+5nZsOqWJZKdOku25JkyK0WhvIqEI2le85DZFhvIZnYF8BXgyHjVEuCqahYlkhkzLOGSV8qsFIbyKhKOCvKah8wmmcViZ3ff3syeBXD3eWa2RpXrEslMDnK5upRZKQzlVSQcIeU1SQN5mZnVEV00gJn1AFZUtSqRjBhhTWTeBGVWCkF5FQlHaHlNMgb5SuBPQC8zOw94DPhVVasSyVA0V2PLS44ps1IYyqtIOJLmNQ+ZbfEMsrtfb2bPAHvFqw5195eqW5ZINszCmoamMcqsFIXyKhKO0PKa9E567YBlRF1AiWa+EAlVSF1AzVBmpRDSzKuZnQx8lyg3LwLfAfoAE4AewDPAke7+eWo7jSivUgghfb8mmcXiTOAWYAOgH3CzmZ1e7cJEsmIJlxbfx+xkM5tqZi+Z2S1m1snMNjKzJ81smplNrMbFOMqsFEmKee0LnAjs4O5DiRqto4mGO1zi7psC84HjUq1feZUCSZrXPDSjkxypHgXs6O5nufuZwHDgmKpWJZKhNKagyerLNqbMSmGkPGVUe6CzmbUH1gRmAXsCt8evXwccnPJHUF6lMEKa5i1JA3kWDYditI/XibQ50VW2qU1insWXLSizUhBp5tXdZwL/D3iPKC8LiYZULHD35fFmM4C+KX8M5VUKoZK8JslstXtpmxyDbGaXEI2HmgdMNbP74+d7A0+3dociuVbZkWtPM5tc8nycu4+D6MvWzOq/bJcCf6fKX7bKrBROSnmN3srWAQ4CNgIWALcBo9IqtZzyKoWT4pnhkl7aLdx9qZndStRLux9RL+0EM7uKqJf2d63ZR3MX6dVfRTsVuLtk/aTW7EgkFBVcZTvX3Xdo7IVaf9nGlFkpnDTyGtsLeNvdPwQwsz8DuwDdzax9fGDbD5i5OvWWUF6lcFKexaK+l3YZDXtpvxW/fh1wLmk3kN39mta8oUjI6ruAUlDrL1tlVgonxbxC1NszwszWJOr1GQlMBh4Cvkk0k8XRwN/S2JnyKkXTirxm2kvb4jRvZrYJ8HNgC6BT/Xp336y1OxXJs5S6gGr6ZVtKmZUiSavL1t2fNLPbgSnAcuBZYBzR2d0JZnZBvC7Vhq3yKkVSYV4z7aVNcpHeeOCPRI3/fYFbgYlpFiGSJ2lMQePuTxJdjDeFaD7VOqIv21OBH5vZNKJ5VatxFmk8yqwURJpTRrn7Oe4+xN2HuvuR7v6Zu7/l7sPdfVN3P9TdP0v5I4xHeZWCSHGat5W9tO6+DGjQSxtvs1q9tEkayGu6+/0A7v6mu59FFGKRNscsmsg8ydKSjL5sQZmVgkgzrxlSXqUQKslrgsyu7KW16LT0SOBl/tNLC6vZS5vkTnqfmVkd8KaZHU/UGu/a2h2K5F2+v0sTUWalMJRXkXCklddaDIlK0kA+GViLaDqNnwNrA8e2docieZeHCcpXkzIrhaG8ioQjzby6+znAOWWr3yK62c5qa7GBHI+lBFgEHJnGTkXyyjDapTsNTc0ps1IUyqtIOELLa3M3CvkL0aTljXL3b1SlIpEsWbhdtsqsFI7yKhKOwPLa3BnkK2pWRWzTnmvx17E71Xq3wVpnxxOyLiEYn733QeJtA+6yrWlmtxvcj8cfu6iWuwya8lodymsyWw/pz4OPXFrLXQZtnb1/kXUJbVJIeW3uRiEP1rIQkbxIMrVLHimzUkTKq0g4Qsprkov0RArDCOsIV6TIlFeRcISWVzWQRcoEdA2BSOEpryLhCCmviRvIZtaxSjc1EMmVkALcHGVWikB5FQlHSHltcTiImQ03sxeBN+Ln25jZ/1W9MpEMmEG7Oku05JUyK0WhvIqEo5K85iGzScZLXw4cAHwE4O7PA1+pZlEiWTJLtuSYMiuFobyKhCNpXvOQ2SRDLOrc/d2ygdVfVKkekUwZJLkHfN4ps1IIyqtIOELLa5IG8nQzGw64mbUDfgi8Xt2yRLIT0jQ0TVBmpTCUV5FwhJTXJA3k/yLqAtoQ+AD4R7xOpE0K6AC3KcqsFIbyKhKOkPLaYgPZ3ecAo2tQi0jmzCyoLqDGKLNSFMqrSDhCy2uLDWQz+wON3C/e3cdWpSKRjLULqQ+oEfcLtyMAACAASURBVMqsFInyKhKOkPKaZIjFP0oedwK+DkyvTjki2QrtIoImKLNSCMqrSDhCy2uSIRYTS5+b2Q3AY1WrSCRjAeW3UcqsFInyKhKOkPLamltNbwT0TrsQkVywsO70k5AyK22T8ioSjsDymmQM8nz+Mz6qDpgHnFbNokSyZASU4EYos1IkyqtIOELKa7MNZItmLt8GmBmvWuHuq1xMINJWRGOksq6i9ZRZKRLlVSQcoeW12esJ46De4+5fxIuCK21enSVb8kiZlaJRXkXCkTSvechskgk3njOz7apeiUgOGNCuzhItLb6XWXczu93MXjWzV8zsS2a2rpk9YGZvxH+uU4WPocxKIaSZ1wwpr1IIleQ1D5ltcoiFmbV39+XAdsDTZvYm8AnRZ3R3375GNYrUjqV6le1lwH3u/k0zWwNYEzgDeNDdLzSz04jGGp6axs6UWSmcdPNaU8qrFE5geW1uDPJTwPbAgTWqRSQX0pin0czWBnYHjgFw98+Bz83sIGCPeLPrgH+RUgMZZVYKKM15Vc2sO3A1MJTowrljgdeAicBA4B3gMHefn8LulFcpnJDy2lwD2QDc/c3WvLFIiFK8iGAj4EPgj2a2DfAMcBLQ291nxdvMJt3pnJRZKZQqXPRTy14f5VUKJbS8NtdA7mVmP27qRXf/TWt2KJJ3FRzg9jSzySXPx7n7uPhxe6KzQz909yfN7DLKpm5ydzezNC/KUWalcNI6IZVBr4/yKoUTUl6bayC3A7pAQJPWiaw2oy75X/m57r5DE6/NAGa4+5Px89uJGsgfmFkfd59lZn2AOatXbwPKrBRMRXlt7oAWat/ro7xKwVSUV2g+s1XPa3MN5Fnufn5r31gkRGbQLsncLi1w99lmNt3MBrv7a8BI4OV4ORq4MP7zb6u/t5WUWSmUCvPa3AEt1L7XR3mVQmnF92tzma16XlscgyxSNCleRPBD4KZ4bNRbwHeIpla81cyOA94FDktrZyizUkAp5rXWvT7KqxROSHltroE8srVvKhIqI70xUu7+HNDY0W+1sqXMSqGknNda9/oor1IooeW1yQayu89r7ZuKhCzNaWhqSZmVIko5rzXr9VFepYhCymtzZ5BFCinQ9rFIIaWZ1wx6fUQKJaS8qoEsUsJIdv91Ecme8ioSjtDyqgaySCkD0ylkkTAoryLhCCyvITXma2r69Onss9dX2G7rLdh+my254vLLsi4pF646ZwzvPvhLJt92xiqvnXTknix99gp6dF8LgN2GDWL2IxczacJpTJpwGqePHVXrcitmQDuzRIvkx+uvvcZOw7Zduay3bjf+77JLsy4rc5XkFeDXp3yTl/52Dk9NPJ1th/SrZamtoryG5cT/+i5DNtqAXYdvu3Ld/HnzOOTAUey47eYccuAoFsxP4y7e4brqp/vz7p9OYvI131u57uzv7M5Tf/guk8Ydx50XjaZPjy4AnHz4TkwadxyTxh3H5Gu+x+IHTmOdrp2yKr1FleQ1D5lVA7kJ7du358KLfs2zL7zMw49N4vdXXckrL7+cdVmZu+HOSRz0gytXWd+vd3dGjtic92Y1vO7k8WffZMToCxkx+kJ+Oe6+WpW5WizhIvmx2eDBPPnMczz5zHP8+6lnWHPNNTnw4K9nXVbmKsnrPrtuwSYb9mLoQedxwgW3cPkZo2tZaqspr+EYPeZoJv7lrgbrLvvNRez+5T15+rlX2P3Le3LZby7KqLp8uOH+FzjotAkN1l0ycRLDv3c1I8Zew71PTOP0I3eN1z/JiLHXMGLsNZx99UM8+sJ7zF/0aRZlJ5Y0r3nIrBrITejTpw/bbb89AF27dmXIkM15//2ZGVeVvcenvMm8hUtWWX/RTw7hzMv+inuad07OhlmyRfLpoX8+yEYbb8KAAQOyLiVzleT1gC9vzc13PQXAUy++w9pdO7N+z241q7W1lNdw7LzrbqyzzroN1t17950cPuZIAA4fcyT33HVHFqXlxuMvTGfexw0buYuWfL7y8ZqdOtDYt+xhe27Jrf/M/0m8pHnNQ2Y1BjmBd995h+eee5Ydh++UdSm5dMAeW/H+nAW8+PqqBxA7bb0RT048jVkfLuT03/yFV96anUGFlbCgxkjJqm6bOIHDDj8i6zJyq6m8brBed2bM/k/39swPFrDBet2ZPffjWpdYAeU1dB9++AHrr98HgN691+fDDz/IuKJ8OvfYLzNm761Y+MlnjPrxTQ1e69yxPV/dcWNOvvz+jKpLKqy8Vu0Msplda2ZzzOylau2jFhYvXswRhx3Cxb++lG7d8n82pdY6d+rAKcfuw/m/u3uV1557dTqD9/tfdjr8Qn434WFuvWRsBhVWpv4q2yRLW9MWMvv5559z91138I1vHpp1KbnUXF5DpLyGnddyZmE1oGrp3GsfZtDoK5jwj5c4/uBhDV7b/0uDeGLqjCCGVyTNax4yW80axgP5vyqrGcuWLeOIww7h8CPGcPDXv5F1Obm0cb9eDOjbg6cmns6rd59H3/W688TNp9K7R1cWffIpnyyNuobuf+xlOrRv1+CCoLyq/0e6paUNGk/gmb3/vnvZdrvt6d27d9al5FJzeX1/zgL6rb/Oym379u7O+3MWZFhtMspr2Hr16s3s2bMAmD17Fj17rpdxRfk28cGpHLz7kAbrDt1zC257cGpGFVUmaV7zkNmqNZDd/REg2DsFuTvHf+84Bg/ZnJNO/nHW5eTW1GnvM2Dk6QzZ/xyG7H8OM+cs4Evf+hUffLSI3j26rtxuhy0HUGfGRws+ybDaBCy600+Spa0JPbMAt068RcMrmtFcXu9++EW+dcBwAIZvNZCPFy/N+fAKlNfA8wowar8DmHjTDQBMvOkG9t3/axlXlD+b9P3PgesBu2zG6+99tPJ5t7U6suvWG3Lnv9/IorTKVJDXPGQ28zHIZjYWGAvQf8MNM67mP/79+OPcfNMNDB26FTsNi6akOe+CXzBq3/0yrixb1/3yGHYbNoie3bsw7b6f8bOr7uG6vz7R6LZf32s7vnfobiz/4gs+/XQZR53+xxpXW7nQJjKvtbzmFeCTTz7hn/94gCt++/usS8mNSvJ632NT2WfXLZl6xzks+XQZ3z/3xhpXWznltXmlee3XP/u8fu873+bxRx9m3kdz2WrwQE4942xO+vEpHHf0Edx4wx/p339DrrnulqzLzNR1Zx3EbtsMoOfanZk28QR+Nv5RRu20CYP692DFCue9OQs58ZJ7V25/4K6b8eDkt1ny6bIMq04mtLxaNWcdMLOBwF3uPjTJ9sOG7eCPPzm5avW0NevseELWJQTjs9duZcWSOS0ekm665TZ+8S3JLnT4xjZ9nnH3xm5zGaxKMqu8VkZ5rcynz13ZYr6UVxtIwrxuu/0wf/CRJ6teU1vR78BiTzdXqU8fOjPVvEL2mc38DLJI3mTfsSMiSSmvIuEIKa9qIIuUycHQJxFJSHkVCUdIea3mNG+3AE8Ag81shpkdV619iaQlGiNliZa2RpmV0CivyquEo5K85iGzVTuD7O66lFyCFNIRbpqUWQmR8ioSjpDyqiEWIg3kY3oZEUlCeRUJR1h5VQNZpER9F5CI5J/yKhKO0PKqBrJIKQurC0ik0JRXkXAEllc1kEXKhBRgkaJTXkXCEVJe1UAWKWMBdQGJFJ3yKhKOkPIa0l3/RKrOgDpLtiR6P7N2Zvasmd0VP9/IzJ40s2lmNtHM1qjixxFp09LOq4hUTyV5zUNm1UAWKWMJ/0voJOCVkue/Ai5x902B+YDmLhVZDSnnVUSqKGlek2a2mieh1EAWKVNnlmhpiZn1A/YHro6fG7AncHu8yXXAwVX6GCKFkFZeRaT6kua1gsxW7SSUGsgiJVLusr0UOAVYET/vASxw9+Xx8xlA37Q/g0hRaIiFSDjSHmJR7ZNQaiCLNFBR909PM5tcsoxd+S5mBwBz3P2ZzD6KSJuXbnct6LoBkeqpZIBF89+xsaqehNIsFiKlKpunca6779DEa7sAB5rZfkAnoBtwGdDdzNrHAe4HzFzNikWKqzrzqtZ32XaLn9d32U4ws6uIumx/l/peRdq6yvPa5Hds6UkoM9sjhepWoTPIImUs4dIcdz/d3fu5+0BgNPBPdx8DPAR8M97saOBvqX8AkQJJI68r30vXDYhUVdK8Jshs/Umod4AJRDldeRIq3ma1TkKpgSxSIhojVdWLfk4Ffmxm04i6g65Jq3aRoqlCXnXdgEiVVJLXljJbi5NQGmIhUibtLlt3/xfwr/jxW8DwdPcgUlwV5LWnmU0ueT7O3cf9532q32UrUnQ1mFDmVGCCmV0APMtqnIRSA1mkjOZMFQlHBXlt7poB0HUDIlVXje/Xap2E0hALkTJmyRYRyV5aedV1AyLVlzSvefiOVQNZpEyaF/2ISHXVIK+6bkAkJSlepFd1GmIhUi4PyRSRZKqQV103IFIlAX2/qoEsUiI6cg0owSIFpryKhCO0vKqBLFIqJ2OfRCQB5VUkHIHlVQ1kkTIhBVik6JRXkXCElFc1kEUasKC6gESKTXkVCUdYeVUDWaRMSEe4IkWnvIqEI6S8qoEsUiIv08uISMuUV5FwhJZXNZBFyoWUYJGiU15FwhFQXtVAFikT0hgpkaJTXkXCEVJe1UAWKVMXTn5FCk95FQlHSHlVA1mkVGiDpESKTHkVCUdgeVUDWaRMSF1AIkWnvIqEI6S8qoEsUsIIaxoakSJTXkXCEVpe1UAWKRNQfkUKT3kVCUdIeVUDWaRcSAkWKTrlVSQcAeU1Vw3kKVOemdu5g72bdR1legJzsy4iIHn9fQ1IumFIY6SylNO8Qn7/DuZVXn9fiTKrvCbz/LNT5vbs2kF5DV9ef19tLq+5aiC7e6+sayhnZpPdfYes6whFW/h9hTQNTZbymFdoG38Hayn035fymozy2jaE/vsKKa+5aiCL5EJAARYpPOVVJBwB5VUNZJES0TSNASVYpMCUV5FwhJbXuqwLCMC4rAsITNi/L4umoUmyNPs2Zv3N7CEze9nMpprZSfH6dc3sATN7I/5znVp8rIIJ++9g7YX7+0opr5KpcP/+ZSPc31cFec1DZtVAboG7h/uXMQNt4fdlCZcWLAf+x923AEYAPzCzLYDTgAfdfRDwYPxcUtQW/g7WUui/r5TyKhkJ/e9frYX++0qa15YyW4uTUGogi5RLIb3uPsvdp8SPFwGvAH2Bg4Dr4s2uAw5OvX6RIlELWSQcabWQa3ASSmOQRRqw1MdImdlAYDvgSaC3u8+KX5oN9E51ZyKFkn5eRaRa0str/D06K368yMxKT0LtEW92HfAv4NTW7ENnkJthZqPM7DUzm2Zm6gpvhplda2ZzzOylrGtZHUY0DU2SBehpZpNLlrGrvJ9ZF+BPwI/c/ePS19zdAa/BxyoE5bUybSGzFeZVckaZTa5oeU36HQvVOwmlBnITzKwdcCWwL7AFcER8+l4aNx4YlXURqUje/TPX3XcoWRqMDTOzDkSN45vc/c/x6g/MrE/8eh9gTvU/UNunvLbKeNpCZjXEIkjKbMXGU6S8JviOheqehFIDuWnDgWnu/pa7fw5MIDp1L41w90eAeVnXkQZL+F+z72FmwDXAK+7+m5KX7gCOjh8fDfytKh+ieJTXCrWVzKaRV8mEMluBouU1SWarfRJKDeSm9QWmlzyfEa+TNi6lKWh2AY4E9jSz5+JlP+BC4Ktm9gawV/xcVp/yWlBpTRmlqRlrTpktoLSmeavFSShdpCdSJo1zTe7+WDNvNTKFXYgIqY6eqL8qfoqZdQWeMbMHgGOIroq/MB4nexqtvOhHpOhSzGv9SagXzey5eN0ZRCedbjWz44B3gcNauwM1kJs2E+hf8rxfvE7asoRnmyR3lNciSjGvtbgqXhpQZosm3bxW/SSUGshNexoYZGYbEYV2NPCtbEuSajPA1EIOkfJaQBXmtaeZTS55Pq6pmy5oasaaUGYLJrTvV41BboK7LwdOAO4nusnDre4+Nduq8svMbgGeAAab2Yy4eyNIuig+PMpr5dpKZivIa4tXxIOmZqwVZbYyRctrHr5jdQa5Ge5+D3BP1nWEwN2PyLqGtAR0gCsllNfKtJXMppnX5q6Kd/dZmpoxXcpscspr7ekMskgZTRslEo4Up4zS1IwiVZbmNG/VpjPIIuWyz6WIJJVeXqt+VbxI4QX0/aoGskiZgPIrUnhp5VVTM4pUX0jfr2ogi5RIelMBEcme8ioSjtDyqgaySJmQpqERKTrlVSQcIeVVF+klZGZfxLcLfsnMbjOzNVfjvfYws7vixwfGd2dqatvuZvbfrdjHuWb2k6Try7YZb2bfrGBfA83spUprzKtQpqCRpimvzW6vvEruKLPNbt9mMhvSNG9qICe31N23dfehwOfA8aUvWqTi36e73+HuFzazSXeg4vBK66Vxn3jJnPJaEMprm6HMFkDSvOYhs2ogt86jwKbxUd1rZnY98BLQ38z2NrMnzGxKfBTcBcDMRpnZq2Y2BfhG/RuZ2TFmdkX8uLeZ/cXMno+XnYmuoN4kPrK+ON7up2b2tJm9YGbnlbzXmWb2upk9Bgxu6UOY2ffi93nezP5UdsS+l5lNjt/vgHj7dmZ2ccm+v7+6v8j8CWcKGklMeVVesy5UKqPMtsnMVjLJW/aZVQO5QmbWHtgXeDFeNQj4rbtvCXwCnAXs5e7bA5OBH5tZJ+APwNeAYcD6Tbz95cDD7r4NsD0wFTgNeDM+sv6pme0d73M4sC0wzMx2N7NhRLfq3BbYD9gxwcf5s7vvGO/vFaD0zjwD433sD1wVf4bjgIXuvmP8/t+z6DahbYYRztGttEx5VV6V17Aos203s5XkNQ+Z1UV6yXW2/8yN+SjRhPIbAO+6+6R4/QhgC+Bxi/7vrkF0a8ghwNvu/gaAmd0IjG1kH3sCRwG4+xfAQjNbp2ybvePl2fh5F6IwdwX+4u5L4n3ckeAzDTWzC4i6mLoQ3fKz3q3uvgJ4w8zeij/D3sDW9p+xU2vH+349wb5Eakl5VV4lLMqsMpsraiAnt9Tdty1dEQf0k9JVwAPlt4Q0swY/t5oM+KW7/75sHz9qxXuNBw529+fN7Bhgj5LXvGxbj/f9Q3cvDTlmNrAV+86tujwcusrqUl6VVwmLMluAzIaUVw2xSNckYBcz2xTAzNYys82AV4GBZrZJvF1T91R/EPiv+GfbmdnawCKiI9d69wPHloy76mtm6wGPAAebWWcz60rU1dSSrsAsM+sAjCl77VAzq4tr3hh4Ld73f8XbY2abmdlaCfYTjoC6f2S1Ka+hU16LRpkNWQV5zUNmdQY5Re7+YXyUeIuZdYxXn+Xur5vZWOBuM1tC1H3UtZG3OAkYZ9EtTb8A/svdnzCzxy2a4uXeeIzU5sAT8dH1YuDb7j7FzCYCzwNzgKcTlPy/wJPAh/GfpTW9BzwFdAOOd/dPzexqonFTUyza+YfAwcl+O2GweJG2T3kNn/JaLMps2ELLq7mXn+UXKa7th+3gD//7qUTbduvU7hl336HKJYlIE5RXkXBUklfIPrM6gyxSJg/Ty4hIMsqrSDhCyqsayCJl8jD2SUSSUV5FwhFSXtVAFikTUH5FCk95FQlHSHlVA1mkjIV0iCtScMqrSDhCyqsayCIl6u/0IyL5p7yKhCO0vGoWC5ESZnYf0DPh5nPdfVQ16xGRpimvIuGoMK+QcWbVQBYRERERKaE76YmIiIiIlFADWURERESkhBrIIiIiIiIl1EAWERERESmhBrKIiIiISAk1kEVERERESqiBLCIiIiJSQg1kEREREZESutW0SIl23Qa4L1+aaFtf+uH9ujOXSHaUV5FwVJJXyD6zaiCLlPDlS+k4+LBE23763JWV3DJTRFKmvIqEo5K8QvaZVQNZpAED08gjkTAoryLhCCuvaiCLlDLALOsqRCQJ5VUkHIHlVQ1kkXJ17bKuQESSUl5FwhFQXtVAFmkgrC4gkWJTXkXCEVZe1UAWKRdQF5BI4SmvIuEIKK9qIIuUMoI6whUpNOVVJByB5VUNZJEGLKgjXJFiU15FwhFWXsNpyovUitUlW1p6G7NrzWyOmb3UyGv/Y2ZuZj3j52Zml5vZNDN7wcy2r8InE2l7lFeRcCTNaw4yqwaySDmzZEvLxgOr3AXIzPoDewPvlazeFxgUL2OB36325xApAuVVJBxJ85qDzKqBLFLKLJqGJsnSAnd/BJjXyEuXAKcAXrLuIOB6j0wCuptZnzQ+kkibpbyKhKOSvOYgs2ogi5RLqfun0bc2OwiY6e7Pl73UF5he8nxGvE5EmqO8ioQjxSEWjb59ipnVRXoiDVQ0T2NPM5tc8nycu49r8p3N1gTOIOr6EZHVpryKhKPieZAzzawayCLl6hJfZTvX3Xeo4J03ATYCnrdofFU/YIqZDQdmAv1Ltu0XrxOR5iivIuFInlfIOLMaYiFSqn6exip0/7j7i+6+nrsPdPeBRF0827v7bOAO4Kj4StsRwEJ3n5XmRxNpc5RXkXBUktccZFYNZJFyKV1ha2a3AE8Ag81shpkd18zm9wBvAdOAPwD/ncZHEWnzlFeRcKQ4i0W1M6shFiINWKKrZ5Nw9yNaeH1gyWMHfpDKjkUKQ3kVCUd6eYXqZ1YNZJFyAd0KU6TwlFeRcASUVzWQRUoln6BcRLKmvIqEI7C8qoEsUi6gI1yRwlNeRcIRUF7DqbSKzOxcM7uxBvsZGN8bvOIDEzPbw8xmNPP6eDO7YPUqFCC1CwikepRZWUl5zT3lVVZK91bTVVWIBrKZLS5ZVpjZ0pLnY7Kury0ws5PNbLaZfWxm15pZxwQ/c3b8j9letagxGavaFDSSnDJbfUkzW9LoKP1/8r+1rrdxymseKK/VV8l3rJmtaWa/NbO5ZrbQzB6pZa1NqyCvOchs9hXUgLt3qV+A94Cvlay7qZL3as2RadbMrKuZda7i++8DnAaMBAYAGwPntfAzmwCHAvmbOzSQo9u2TJnNX2aB7iX/D35WrdoqprxmTnnNXV7HAesCm8d/nlyt2iqmM8hBWsPMrjezRWY21cxW3r3FzN4xs1PN7AXgEzNrb2YbmNmfzOxDM3vbzE4s2X64mU2Oj/Q+MLPflO1rjJm9Fx/dnVnycx3N7FIzez9eLm3mrM52ZjYlrnci0KmZzzYUeN/Mfm/RBNlpOxq4xt2nuvt84GfAMS38zJXAqcDnVain9cygrn2yRbKmzLZeazKbP8prSJTX1kucVzMbAhwIjHX3D939C3d/pgo1Va6SvOYgs2og/8eBwASgO9EdV64oe/0IYP/49RXAncDzQF+io7ofxUd5AJcBl7l7N6JbH95a9l67AoPjnzvbzDaP158JjAC2BbYBhgNnlRdqZmsAfwVuIDo6vA04pKkP5u5PANsTna292cxeMbNTzKxP2fvuamYLmll2bWIXW8a/i3rPA73NrEdjG5vZocBn7n5PUzVnKpCjW1Fma5XZ2LsWTcb/RzPr2cx2taW8hkJ5rU1ehwPvAufFBwgvmlmTtdecziAH6TF3v8fdvyAKxTZlr1/u7tPdfSmwI9DL3c9398/d/S2iO7OMjrddBmxqZj3dfbG7Typ7r/Pcfam7P0/0F71+X2OA8919jrt/SNSFcmQjtY4AOgCXuvsyd78deLq5D+fub7v7uUT/mBwPDAFeNrO7zGzDeJvH3L17M8tjTbx9F2BhyfP6x13LNzSzrsAvgJOaqzdTgYyPEmW2FpkF5hL9/gYAw+JtKuo2ryrlNRTKa23y2o/ojPZCYAPgBOC6koOEbGkMcpBmlzxeAnSyhmOhppc8HgBsUHrkB5wB9I5fPw7YDHjVzJ42swNa2FeX+PEGREd+9d6N15XbAJgZ3xmmdNsWxT/zMtE/GjOIjkzXSvKzzVgMdCt5Xv94USPbngvc4O7vrOY+qyeQo1tRZldD4szGDZDJ7r7c3T8g+sLdOz7YzZ7yGgrltfUq+Y5dSnQAcUF8cPEw8BCw92rWkA6dQW6TSoMyHXi77Mivq7vvB+Dub3h0C8T1gF8Bt5tZkoC8T/QPQ70N43XlZgF9zRr8DdqwuTeOx15908zuBN4gOhN0IrCxu78Sb7ObNbwauXzZrYm3n0rDswHbAB+4+0eNbDsSONGiq3FnA/2BW83s1ObqrxnTVfFtiDKbTmbL1f9esw+B8tqWKK/p5PWFRtZ5I+tqr5K85iCz2VcQpqeARRZdVNDZzNqZ2VAz2xHAzL5tZr3cfQWwIP6ZFQne9xbgLDPrFY/xOxtobO7IJ4DlRA3NDmb2DaJxR40ys62JAn8S0biq/u5+lLs/VHqE7O6PesnVyI0sjzaxi+uB48xsCzPrTjSma3wT244k6v7ZNl7eB75PdNFePgRydCsVUWYbSpxZM9vJzAabWV085vFy4F/uvrCx7WtOeW2LlNeGKvmOfYRoJpHTLbrYcRfgK8D9Tf5WaklnkNu2eAzVAUQNvLeJxuhdDawdbzIKmGpmi4kuJhgdj6tqyQXAZKIjwBeBKfG68v1/DnyD6CrWecDhwJ+bed85wHB3383dr3H3xrplWs3d7wMuIurGeY+oK+qc+tctumJ5TLztR+4+u34BvgDmu/viNGtqLQPq6uoSLRIOZXaVehJnlmhKqfuIunNfAj4juqAqc8pr26S8rlJPJd+xy4CDgP2IxiH/ATjK3V9Ns6bWqCSvecisNRxiI1Js7dYd6J1GntPyhsCS2499xt13aHlLEakG5VUkHJXkFbLPbPYTzYnkimE56NoRkSSUV5FwhJVXNZBFyoQUYJGiU15FwhFSXtVAFikTUoBFik55FQlHSHlVA1mkTEgBFik65VUkHCHlNVcNZGvf2W2NfMw9H4LtNm92WkYp8e677zB37tyWk2nxIi1SXiujvFZmypRn5rp7r2Y3Ul4TU14ro7xWpi3mNV8N5DW60nHwYVmXEYzHnyy/lb00ZZedkl0Iu4CT7QAAIABJREFUa1guppcJgfJaGeW1Mp07WIt3LlNek1NeK6O8VqYt5jVXDWSRPAipC0ik6JRXkXCElFc1kEXKhBRgkaJTXkXCEVJewznXLVILVsHS0luZXWtmc8zspZJ1F5vZq2b2gpn9Jb5taP1rp5vZNDN7zcz2SfNjibRJyqtIOCrJaw4yqwaySBkzS7QkMJ7olqilHgCGuvvWwOvA6fE+twBGA1vGP/NbM2uX1mcSaauUV5FwJM1rHjKrBrJICSO98Lr7I8C8snV/d/fl8dNJQL/48UHABHf/zN3fBqYBw9P7ZCJtj/IqEo5K8pqHzKqBLFKmgvD2NLPJJcvYCnd1LHBv/LgvML3ktRnxOhFphvIqEo4KG8iZZlYX6YmUMrC6xBcRzHX3ZPPHle/G7ExgOXBTa35eRFBeRUJSWV4h48yqgSxSJuHYp9V5/2OAA4CR7u7x6plA/5LN+sXrRKQZyqtIOKqd13gfx5BCZjXEQqRMihcQNPbeo4BTgAPdfUnJS3cAo82so5ltBAwCnlrtDyPSximvIuFI+SK9xt4/tczqDLJIifqLCFJ5L7NbgD2IxlHNAM4huqK2I/BAvJ9J7n68u081s1uBl4m6hX7g7l+kUohIG6W8ioQjzbxC9TOrBrJIuZTy6+5HNLL6mma2/znw83T2LlIQyqtIOFIcYVHtzKqBLFLKwrrTj0ihKa8i4Qgsr2ogi5Spq9PQfJFQKK8i4Qgpr2ogi5QL5wBXRJRXkXAElFc1kEXKhNQFJFJ0yqtIOELKqxrIIiVWZ3oZEakt5VUkHKHlVQ1kkTIhBVik6JRXkXCElFc1kEXKhBRgkaJTXkXCEVJe1UAWKRdOfkVEeRUJR0B5VQNZpJSFNQ2NSKEpryLhCCyvaiCLlDAgoB4gkUJTXkXCEVpe1UAWaSCsq2xFik15FQlHWHlVA1mkTED5FSk85VUkHCHlVQ1kkTIhHeGKFJ3yKhKOkPKqBrJIKQvrCFek0JRXkXAEllc1kEVKGNCuXUAJFikw5VUkHKHlVQ1kkTIhdQGJFJ3yKhKOkPKqBrJIqcC6gEQKTXkVCUdgeVUDWaRENE9jQAkWKTDlVSQcoeU1nFuaVMlV54zh3Qd/yeTbzljltZOO3JOlz15Bj+5rAdCtSyduv/T7PDnxNJ65/UyOPHBErcvNre9/91g23GA9hm07NOtSVlM0T2OSRbKhzKajbWRWec075TUdRctrHjJb+AbyDXdO4qAfXLnK+n69uzNyxOa8N2veynXfP2x3Xn1rNjsdfiH7fO8yLvzx1+nQvl0ty82tI48+hr/ddV/WZaTCLNki2VBm09FWMqu85pvymo6i5TUPmS18A/nxKW8yb+GSVdZf9JNDOPOyv+LuK9c50GWtjgCs1bkj8xcuYfkXK2pVaq7tutvurLvuulmXkYpQjm6LSplNR1vJrPKab8prOoqW1zxktvAN5MYcsMdWvD9nAS++PrPB+qsmPMyQjdbnrb//nMm3ncFPLr69QbglfGZQV2eJlpbfy641szlm9lLJunXN7AEzeyP+c514vZnZ5WY2zcxeMLPtq/gx2xxltpiU1zApr8VUSV7zkNmqNpDNbJSZvRYXdFo195WWzp06cMqx+3D+7+5e5bWv7rw5L7w2g433PpOdRv+SS047lK5rdcqgSqmmFLt/xgOjytadBjzo7oOAB+PnAPsCg+JlLPC7ND5LJULMKyizRVfUvEKYmVVeiy3lIRbjqWJmq9ZANrN2wJVxUVsAR5jZFtXaX1o27teLAX178NTE03n17vPou153nrj5VHr36MqRB47gb/98HoC3ps/lnZkfMXhg74wrlrSl1f3j7o8A88pWHwRcFz++Dji4ZP31HpkEdDezPil9pBaFmldQZouuiHmFcDOrvBZbmkMsqp3Zak7zNhyY5u5vAZjZhLjAl6u4z9U2ddr7DBh5+srnr959HruMuYiPFnzC9Nnz2WP4YB5/9k3WW7crmw3szdsz52ZYrVRDBUOfeprZ5JLn49x9XAs/09vdZ8WPZwP1//r3BaaXbDcjXjeL2ggyr6DMFl1B8wqBZlZ5LbYKhxZnmtlqDrFoqpgGzGysmU02s8m+fGkVy2ncdb88hn9d9z9s9v/Zu/O4K+f8j+Ovz30nlSJE2iiVLFGS7MsPQ3ZjLFlDMxkMBmNnLMMwGMsMM2TLvmUJQ7Zh7FHW7JEoUSm00fb5/XFOOffRfd/XubvOua7vfb2f8ziPue/rnM71vW+9Ot/rnGtZoy1jR/yFgXttVutjL75+BJv26sLr957BY9cdy5lXDefb72ZVcLTpdejBB7DtVpvx8Ucf0bVzR4bedGPSQ2oYK+kdqanu3rfgVl+4NXhu57q07GAXRK+gZuPSKJrNbq8QoVn12nhkrdc0NJv4hULyP/AQgKoWq1b8H5+Bpw+t8/61dz1n8deTpnzP7kf/8nQ1ArfeflfSQ4hF7kTmZV3FN2bWzt0n5T/emZxfPhHoVPC4jvllqZJ0r6Bm49IYmlWvdVOvjYd6jSy2Zsv5DnIQ/4CI1FT2U9A8DAzMfz0QGF6w/ND8kbabAt8XfExUCepVApTZXkHNSnAqcqGQ2Jot5zvIrwPdzawLuWgHAAeWcX0isYhyepkozOwuYFty+1FNAM4BLgbuNbNBwHhgv/zDHwN2AcYCs4HDYxlEdOpVgpTRXkHNSoDi6hXK32zZJsjuPt/M/gA8AVQDN7n7e+Van0gsYryCj7sfUMtd2y/hsQ4cE8+aS6deJUgZ7TU/BjUrYYn5Cnnlbras+yC7+2PkZu0iQcjtI5X8FXySoF4lNFnuFdSshCW0XhM/SE8kbUIKWCTr1KtIOELqVRNkkSIB9SuSeepVJBwh9aoJskiRkLZwRbJOvYqEI6ReNUEWKWBmsR5lKyLlo15FwhFar5ogixQJaANXJPPUq0g4QupVE2SRIlUhFSyScepVJBwh9aoJskiRgPoVyTz1KhKOkHrVBFmkgFlYBxGIZJl6FQlHaL3WOkE2s+Xr+oPu/kP8wxFJXkDHENSgZiWL1KtIOELqta53kN8DnNzFTxZZ9L0Dq5dxXCKJCWkLt4ialcxRryLhCKnXWifI7t6pkgMRSQMjrIMICqlZyRr1KhKO0HqtivIgMxtgZmfkv+5oZhuVd1giyamyaLc0U7OSFepVJBxRe01Ds/VOkM3sauD/gEPyi2YD15ZzUCKJMcMi3tJKzUpmqFeRcJTQaxqajXIWi83dvY+ZvQng7tPMrGmZxyWSmBR0ubTUrGSGehUJR0i9RpkgzzOzKnIHDWBmKwMLyzoqkYSEto9ULdSsZIJ6FQlHaL1G2Qf5GuB+YBUzOw94EfhbWUclkqDcuRrrv6WYmpXMUK8i4YjaaxqarfcdZHe/1cxGAzvkF+3r7mPKOyyRZJhBVRqODlgKalayQr2KhCO0XqNeSa8amEfuI6BIZ74QCVVIHwHVQc1KJqhXkXCE1GuUs1icCdwFtAc6Anea2enlHphIUiziLa3UrGSJehUJR9Re09BslHeQDwU2dPfZAGZ2IfAmcFE5ByaSlDScXmYpqVnJDPUqEo6Qeo3yUc4kak6km+SXiTQ6uaNs4zmJuZmdYGbvmdkYM7vLzJqZWRczG2lmY83snjKdzknNSibE2Ssk1qx6lUwopdc0vMbWOkE2syvM7HJgGvCemd1gZtcD7wJTG7pCkVSL6STmZtYBOA7o6+49ye1jOIDc0elXuHs3YDowKL6hq1nJmBgvOlDpZtWrZE6MFwqpRK917WKx6Cja94D/FCx/taErEwlBjJ8ANQGam9k8oAW5d4W2Aw7M338LcC7w75jWp2Ylc2L+xLaSzapXyZyQeq11guzuNzbkCUVCZkB19NPQtDGzUQXfD3H3IQDuPtHMLgO+AOYATwKjge/cfX7+8ROADrEMHDUr2RNXr1D5ZtWrZE2JvULCr7H1HqRnZl2BC4F1gWaLlrv7Wg1dqUialXAQwVR371vLc6wI7Al0Ab4D7gP6xzLAeqhZyZI4es0/TyLNqlfJkhIP0kv0NTbKQXpDgZvJTf53Bu4F7olzECJpEtMpaHYAxrn7FHefBzwAbAG0NrNFG6YdgYkxDx/UrGRIjKeMSqrZoahXyYgYT/NW9l6jTJBbuPsTAO7+qbufRS5ikUbHLHci8yi3enwBbGpmLSy3ybw98D7wLLBP/jEDgeFl+DHUrGRCjL1Ccs2qV8mEUnpNw2tslPMg/2RmVcCnZvZ7crPxVg1doUjaxXEQgbuPNLNhwBvAfHLnNR1C7mCcu83sgvyycuyHqGYlM+I66CfBZtWrZEZIvUaZIJ8ALEfudBoXAisARzR0hSJpF9eJzN39HOCcosWfAf1iWUHt1KxkRpwXHkioWfUqmRFSr/VOkN19ZP7LGcAhcaxUJK0MK/Uo29RRs5IV6lUkHKH1WusE2cweBLy2+91977KMSCRJFvt5GitGzUrmqFeRcATWa13vIF9dsVHkrd+jE088d3mlVxusFXe/MukhBOOnsd9EfmxI14ovUtFm11urEw8/dVklVxm0lQbclPQQGiX1Gs16a3XkoScureQqg7bKwbckPYRGKaRe67pQyDOVHIhIWkQ5tUsaqVnJIvUqEo6Qeo1ykJ5IZhhhbeGKZJl6FQlHaL1qgixSJKBjCEQyT72KhCOkXiNPkM1sWXf/qZyDEUmDkAKui5qVLFCvIuEIqdd6dwcxs35m9i7wSf77Xmb2z7KPTCQBZlBdZZFuaaVmJSvUq0g4Suk1Dc1G2V/6H8BuwLcA7v428H/lHJRIksyi3VJMzUpmqFeRcETtNQ3NRtnFosrdxxftWL2gTOMRSZRBlGvAp52alUxQryLhCK3XKBPkL82sH+BmVg0cC3xc3mGJJCek09DUQs1KZqhXkXCE1GuUCfJR5D4CWh34Bng6v0ykUQpoA7c2alYyQ72KhCOkXuudILv7ZGBABcYikjgzC+ojoCVRs5IV6lUkHKH1Wu8E2cyuZwnXi3f3wWUZkUjCAup3idSsZIl6FQlHSL1G2cXi6YKvmwG/Br4sz3BEkmVAkxScXmYpqVnJBPUqEo7Qeo2yi8U9hd+b2W3Ai2UbkUjCQtrCXRI1K1miXkXCEVKvDbnUdBegbdwDEUkFC+tKPxGpWWmc1KtIOALrNco+yNP5ef+oKmAacFo5ByWSJCOggpdAzUqWqFeRcITUa50TZMudubwXMDG/aKG7/+JgApHGInci86RH0XBqVrJEvYqEI7Re6zxncz7Ux9x9Qf6mcKXRq7JotzRSs5I16lUkHFF7TUOzUfZBfsvMNnT3N8s+GpGEGVCdhjKXjpqVTFCvIuEIrddaJ8hm1sTd5wMbAq+b2afALHI/o7t7nwqNUaRyLL6jbM2sNXAD0JPcPoZHAB8B9wCdgc+B/dx9ekzrU7OSLTH2CpVtVr1K5gTWa13vIL8G9AH2aMgTi4Qqxiv9XAWMcPd9zKwp0AI4A3jG3S82s9PIHYxzakzrU7OSOTFfmauSzapXyZyQeq1rgmwA7v5pQ55YJERxHURgZisAWwOHAbj7XGCume0JbJt/2C3Ac8Q3QVazkilxHvSTQLPqVTIltF7rmiCvYmYn1nanu1/ekBWKpF1MG7hdgCnAzWbWCxgNHA+0dfdJ+cd8TbznO1WzkjkxviFV6WbVq2ROSL3WdRaLaqAl0KqWm0gjZFRFvAFtzGxUwW1wwRM1Iffx6b/dfUNy+xbWOLdp/oj1OI9aV7OSMbH1CpVvVr1KxkTvNQ2vsXW9gzzJ3c9v6BOLhMgoaQt3qrv3reW+CcAEdx+Z/34YuXi/MbN27j7JzNoBk5dmvEXUrGRKjL1C5ZtVr5IpJfYKCb/G1vUOcjjn4hCJi0GTKot0q4u7fw18aWY98ou2B94HHgYG5pcNBIbHO3qRDImpV0ikWfUq2VJCr2l4ja3rHeTtG/qkIqFqwBZuXY4F7sgfXfsZcDi5jdJ7zWwQMB7YL7a1qVnJmJh7hco2q14lU0LrtdYJsrtPa+iTioQsrtPQuPtbwJI+HirLC6OalSyK87RRlWxWvUoWhdRrlCvpiWRKzFu4IlJG6lUkHCH1qgmySAGj7h3zRSQ91KtIOELrVRNkkUIGFtImrkiWqVeRcATWqybIBU44ZjBPPfEYbVZZhedeeROAv11wLk889ghVVVWsvMoqXPWvG1itXftkB5qga0/4FTv368KU72bT96jbAfjzIZux22ZrsnAhTPl+NoP//iSTps1a/Gc2Wqstz12+P4de/BgPvjg2qaFHYkB1QAFn3SnHHcmzTz3Oym1WYcQLowH4YMw7nHXyscyaNYuOndbgimtvplWr5RMeaTKuPXpL+m/UiSnf/8jGJz4IwIWHbMwufTsxd/5Cxn09gyOveYHvZ8+lSbXxr6O2pHeXlWlSXcWd/xvLZQ++k/BPUDf1GpavJk7g5D/8lqlTJ2NmDDj4CA4bfAyPPfwA/7jsQj79+EMeGPE86/feKOmhJuZfR25O/z4dmfLDj2xy8sMAXHDQRuzcpxNz5y9g3DczOeraF/l+9jwA1lt9Rf7x201p1bwpC93Z5sxH+WnewiR/hFqF1mtI73aX3X4HHsKdwx6psezo407kvy+P5ukXX+dXO+3C5ZdcmNDo0uG2p95nz7MerLHsivtH0+/oO9j0D3fw+MhxnH7gJovvq6oyLjh8S55+Y3ylh9pgFvEmydtnwCHcfHfNs/icdsJRnHLWBYx4fhQ77rIH1199RUKjS95tz37CXhc8WWPZf9+ZSN8THmSTkx7ik0nf86e9NwBg7826sOwy1fQ76SG2OGU4g37Vg9VXaZnEsEuiXsPRpEk1p593EU+88AbDHnuO22++jk8++oC11l6Xf910FxtvtmXSQ0zcHf/7lF9f9HSNZf99dxL9Th7OZqc+wtivv+ekvdYHoLrKuOGYLTn+hlfpd/Jwdjn/CebNj/PaU/GL2msamtUEucBmW2zFiiuuWGNZq+V/fudp9uzZQX08UA4vjZnItBk/1Vg2Y/bcxV+3aLZMjcvWHL1Hbx566ROmfDenQiNcembRbpK8fptvSesVV6qxbNynY+m3ee6Fdsttt2PEow8lMbRUeOmDb5g2s2avz7z9FQsW5ip9/eMpdFh5OQDcYbllm1BdZTRv2oS58xcyY87cXzxn2qjXcKzath09N9gQgJYtW9G1ew+++foruq21Nmt2Wyvh0aXDSx9+w/RZNZv97zsFzX4ylfYr5ZrdfoP2jPliOmO+mA7AtJk/sdBTPkGO2GsamtUuFhFc9Jc/M+zuO2i1/PIMe+TJ+v9ABp07cHMO2n4dvp/1E/1Pux+A9isvxx6bd2WnU4dx3VqrJTzCqCzzG0GhW2vtdXjq8UfYcZc9eOzhB5g0cULSQ0qtQ7frzrCXxgHw4Kvj2K3f6nx2/QBaLNuEU4e+xvSZaZ8gq9dQTfhiPO+PeZtefTZOeihBOWTbbtz/yucAdGu3PA48ePoOtFm+Gfe/PI4rH3kvyeHVI6xey/YOspndZGaTzWxMudZRKaeffT6j3/uUvfc9gJuH/Dvp4aTSube8TPdDb+TuZz/i97v3AuDSI7fhrJteJOUbtDUsOso2yq2xaSzN/u2q67j95iHssf3mzJo5k2WaNk16SKl0yt69mL/AufuFTwHo220VFix0ug6+m3WPvo/jdu9J51VbJTzKuqnXMHudNWsmxww6gLP+cklmjw9oiD/ttT7zFzj3vPgZAE2qq9isx6r89uoX2PGcx9l949XZpmd634wqpdc0NFvOMQwF+pfx+Stu730H8J9HHqz/gRl2z7MfstcW3QDo070tt562Cx8OPYJfb9mNK4/Zjt0365rwCOtnZpFujdBQGkGzXbv34Nb7HuXhZ15m9733Y/XOXZIeUuocvG03dt6oE4df9dziZftv1ZWn3pzA/AXOlB9+5NWPvqFP1zbJDTIi9RqWefPmccwRB7LHbwaw0657JT2cYBy0TVd27tORQVc/v3jZxG9n8fIH3/DtjJ+YM3cBT7w1kd6dV05wlPWL2msami3bBNndnweCv1LQZ59+svjrJx57hG7de9Tx6Gzq2r714q9322xNPp6Q2x9qncNvZu3DbmLtw27iwRfH8sdr/ssjr3ya1DAjC+UAgrg1lmanTpkMwMKFC7nm8os5cODvEh5RuvyqdwdO2HN99v3b08yZu2Dx8i+nzmTbnu0AaLFsEzbuvgoff/VdUsOMTL2Gw905/YSj6Na9B4N+f1zSwwnGDr3a88fde7L/pf+t0ewz73zFuquvSPOm1VRXGVuu05YPJ6a72ZAO0kt8H2QzGwwMBujQafVEx3LUoEN4+cXnmfbtVPqsuyZ/Ou1snnlqBJ+O/Zgqq6Jjp9X52xVXJzrGpN1y6s5stUFH2izfjLG3DeIvt71K/407073jiix054vJMzjun88kPcwGMwvrNDSVVthr+46dEh4NHDf4UEa+9ALTp01l8w26cvwpZzN71kxuu+k6AHbadU/2PfDQhEeZnKF/3Jat11uNlVs145Pr9ueCe97gT7/uxbLLVPHo2TsB8NonUzhuyMtcN+IDrjtmK0Zd8WuM3BkwxoyfnuwPUA/1Wre09Tr6tVd46L476bFOT3bfLne2o5POOI+5c3/ivDNOYtq3U/ntQb9hnZ4bMPSehxMebTJuOnZrtlq3LSu3asaH1+zDX4e9xYl7rs+yy1Qz/MwdAXj9kyn88cZX+W7WXK7+z/v878LdcJwn35zIE29OTPgnqF1ovZqXcQdRM+sMPOruPaM8vteGG/kTz71StvE0Nl0G/CvpIQTjp5f/zsLvv6y3zG7r9fJL73oi0nPu3avdaHdf0nXgg1VKs+v33sgffvqlso+psVjv93cmPYSgzLl/UL19qVfrTORe+/hDT6rXqHofd2/SQwjKzHsOi7VXSL7ZxN9BFkmbcLZvRUS9ioQjpF41QRYpEtAnQCKZp15FwhFSr+U8zdtdwCtADzObYGaDyrUukbjkTkNjkW6NjZqV0KhX9SrhKKXXNDRbtneQ3f2Acj23SDmFtIUbJzUrIVKvIuEIqVftYiFSg2Ep2HIVkSjUq0g4wupVE2SRAkZYp6ERyTL1KhKO0HrVBFmkkIX1EZBIpqlXkXAE1qsmyCJFQgpYJOvUq0g4QupVE2SRIiHtIyWSdepVJBwh9aoJskgBA6rC6Vck09SrSDhC67Vs50EWCZVF/F+k5zKrNrM3zezR/PddzGykmY01s3vMrGlZfxiRRi7OXkHNipRT1F7T8BqrCbJIkSqzSLeIjgc+KPj+b8AV7t4NmA7o5P4iSyHmXkHNipRN1F7T8BqrCbJIgUUfAUW51ftcZh2BXYEb8t8bsB0wLP+QW4C9yvKDiGRAnL2CmhUpp1J6TcNrrPZBFqmhpI9j25jZqILvh7j7kILvrwROAVrlv18Z+M7d5+e/nwB0WJrRimRbrL2CmhUpo5IvFJLoa6wmyCKFSjtP41R377vEpzHbDZjs7qPNbNuYRicihWLqFdSsSNmVfh7kRF9jNUEWKRLTQbZbAHuY2S5AM2B54CqgtZk1yW/hdgQmxrM6kWyK8aB4NStSZiH1qn2QRQrk9pFa+gMI3P10d+/o7p2BAcB/3f0g4Flgn/zDBgLDy/jjiDRqcfUKalak3ErpNQ2vsZogixSxiLcGOhU40czGkttf6salG61ItpW5V1CzIrGJ2msaXmO1i4VIEYv5Wpju/hzwXP7rz4B+sa5AJMPi7hXUrEi5hNSrJsgiRUK6VrxI1qlXkXCE1KsmyCJFAupXJPPUq0g4QupVE2SRYiEVLJJ16lUkHAH1qgmySIHcwQEBFSySYepVJByh9aoJskih0k9kLiJJUa8i4QisV02QRYqEFLBI1qlXkXCE1KsmyCI1lHyteBFJjHoVCUdYvWqCLFIkpC1ckaxTryLhCKlXTZBFCsRw1S0RqRD1KhKO0HrVBFmkWEgFi2SdehUJR0C9aoIsUiSkfaREsk69ioQjpF41QRYpEtI+UiJZp15FwhFSr5ogixQK7DyNIpmmXkXCEVivmiCLFAnpIyCRrFOvIuEIqVdNkEUKGGFt4YpkmXoVCUdovWqCLFIkoH5FMk+9ioQjpF41QRYpFlLBIlmnXkXCEVCvqZogv/PWG1PbtV52fNLjKNIGmJr0IAKS1t/XGlEfGNI+Ukka8/YbU9dcpXnaeoX0/h1Mq7T+viI1q16jGfP2m1O7tW2hXsOX1t9Xo+s1VRNkd18l6TEUM7NR7t436XGEojH8vkLaRypJaewVGsffwUoK/felXqNRr41D6L+vkHpN1QRZJA1CClgk69SrSDhC6lUTZJECuWvFB1SwSIapV5FwhNZrVdIDCMCQpAcQmLB/X/kTmUe51fk0Zp3M7Fkze9/M3jOz4/PLVzKzp8zsk/z/r1iJHytjwv47WHnh/r5i6hXUbILC/fuXjHB/XyX0mobXWE2Q6+Hu4f5lTEBj+H1ZxFs95gMnufu6wKbAMWa2LnAa8Iy7dweeyX8vMWoMfwcrKfTfV0y9gppNROh//yot9N9X1F7T8BqrCbJIsRjqdfdJ7v5G/usZwAdAB2BP4Jb8w24B9op9/CJZEtOrrZoVqYCYZsiV6FX7IIvUYKXsI9XGzEYVfD9kSVv3ZtYZ2BAYCbR190n5u74G2jZ8rCJZF3+voGZFyqOkXiHh11hNkOtgZv2Bq4Bq4AZ3vzjhIaWWmd0E7AZMdveeSY+noQyoit7v1PpOt2NmLYH7gT+6+w9WsGOVu7uZeQOHKkXUa2kaQ7Nx9wpqtpLUbHQZ7BUSfo3VLha1MLNq4BpgZ2Bd4ID8/i2yZEOB/kkPIhYxfWRrZsuQC/cOd38gv/gbM2uXv78dMDnm0WeSem2QoTSGZmPcCVnNVo6aLdlQstRrCl5jNUGuXT9grLt/5u5zgbvJ7dsiS+DuzwPTkh4mKuWWAAAgAElEQVRHHCzi/+p8jtxm7I3AB+5+ecFdDwMD818PBIaX5YfIHvVaosbSbBy9gppNgJotQdZ6TcNrrHaxqF0H4MuC7ycAmyQ0FqmgmE5kvgVwCPCumb2VX3YGcDFwr5kNAsYD+8WyNlGvGRXjhQfUbGWp2QwKqVdNkEWKxNGvu79Yx1NtH8MqRIR4egU1K1IJIfWqCXLtJgKdCr7vmF8mjZnFuoUrlaNes0i9hkzNZk1gvWof5Nq9DnQ3sy5m1hQYQG7fFmn0YjzqRypFvWaWeg2Ums2kGI/SKzNNkGvh7vOBPwBPkDsB9b3u/l6yo0ovM7sLeAXoYWYT8vv/BGfRaWii3CQ91GvpGkOz6jVcarY0Wes1Dc1qF4s6uPtjwGNJjyME7n5A0mOIS0gfAcnP1GtpGkuz6jVcajY69Vp5miCLFCnxSj8ikiD1KhKOkHrVBFmkWDj9ioh6FQlHQL1qgixSJKB+RTJPvYqEI6ReNUEWKWCBnYZGJMvUq0g4QutVE2SRIhZSwSIZp15FwhFSrzrNW0RmtsDM3jKzMWZ2n5m1WIrn2tbMHs1/vYeZnVbHY1ub2dENWMe5ZvanqMuLHjPUzPYpYV2dzWxMqWNMqzDO0Ch1Ua91Pl69Suqo2Tof32iaDecsyJogl2KOu/d2957AXOD3hXdaTsm/T3d/2N0vruMhrYGS45WGW/QxUH03STX1mhHqtdFQsxkQtdc0NKsJcsO8AHTLb9V9ZGa3AmOATma2o5m9YmZv5LeCWwKYWX8z+9DM3gD2XvREZnaYmV2d/7qtmT1oZm/nb5sDFwNd81vWl+Yfd7KZvW5m75jZeQXPdaaZfWxmLwI96vshzOx3+ed528zuL9pi38HMRuWfb7f846vN7NKCdR+5tL/I9LHI/5NgqFf1mvRApTRqtlE2G73XNDSrCXKJzKwJsDPwbn5Rd+Bf7r4eMAs4C9jB3fsAo4ATzawZcD2wO7ARsFotT/8P4H/u3gvoA7wHnAZ8mt+yPtnMdsyvsx/QG9jIzLY2s43IXaqzN7ALsHGEH+cBd984v74PgMIr83TOr2NX4Nr8zzAI+N7dN84//+/MrEuE9QTDCGfrVuqnXtWreg2Lmm28zZbSaxqa1UF60TU3s7fyX78A3Ai0B8a7+6v55ZsC6wIvWe6/blNyl4ZcGxjn7p8AmNntwOAlrGM74FAAd18AfG9mKxY9Zsf87c389y3JxdwKeNDdZ+fXEeWa9j3N7AJyHzG1JHfJz0XudfeFwCdm9ln+Z9gR2MB+3ndqhfy6P46wLpFKUq/qVcKiZtVsqmiCHN0cd+9duCAf6KzCRcBTxZeENLMaf24pGXCRu19XtI4/NuC5hgJ7ufvbZnYYsG3BfV70WM+v+1h3L4wcM+vcgHWnVhq2XGWpqVf1KmFRsxloNqRetYtFvF4FtjCzbgBmtpyZrQV8CHQ2s675x9V2TfVngKPyf7bazFYAZpDbcl3kCeCIgv2uOpjZqsDzwF5m1tzMWpH7qKk+rYBJZrYMcFDRffuaWVV+zGsCH+XXfVT+8ZjZWma2XIT1hMOgyizSTYKnXkOnXrNGzYashF7T0KzeQY6Ru0/JbyXeZWbL5hef5e4fm9lg4D9mNpvcx0etlvAUxwNDzGwQsAA4yt1fMbOXLHeKl8fz+0itA7yS37qeCRzs7m+Y2T3A28Bk4PUIQz4bGAlMyf9/4Zi+AF4Dlgd+7+4/mtkN5PabesNyK58C7BXttxOGtJxeRspPvYZPvWaLmg1baL2ae/G7/CLZ1Wejvv6/l1+L9Njlm1WPdve+ZR6SiNRCvYqEo5ReIflm9Q6ySJE0nF5GRKJRryLhCKlXTZBFiqRg1ycRiUi9ioQjpF41QRYpElC/IpmnXkXCEVKvmiCLFLGQNnFFMk69ioQjpF41QRYpsOhKPyKSfupVJByh9aqzWIgUMLMRQJuID5/q7v3LOR4RqZ16FQlHib1Cws1qgiwiIiIiUkBX0hMRERERKaAJsoiIiIhIAU2QRUREREQKaIIsIiIiIlJAE2QRERERkQKaIIuIiIiIFNAEWURERESkgCbIIiIiIiIFNEEWERERESnQJOkBiKRJ9fJruM+fE+mxPmfKE7p0rUhy1KtIOErpFZJvVhNkkQI+/0eWXXtApMf++OY/S7mmvIjETL2KhKOUXiH5ZjVBFilkgFnSoxCRKNSrSDgC61UTZJFipl3zRYKhXkXCEVCvmiCL1GBQVZ30IEQkEvUqEo6wetUEWaRYQB8BiWSeehUJR0C9aoIsUsgI6iMgkUxTryLhCKxXTZBFarCgtnBFsk29ioQjrF7DmcqLVIpVRbvV9zRmN5nZZDMbs4T7TjIzN7M2+e/NzP5hZmPN7B0z61OGn0yk8VGvIuGI2msK3mlOfgQiaWMW7Va/ocAvTnJuZp2AHYEvChbvDHTP3wYD/17qn0MkC9SrSDii9pqCd5o1QRYpZPmjbKPc6uHuzwPTlnDXFcApgBcs2xO41XNeBVqbWbs4fiSRRku9ioSjlF4jNFvuT300QRYpVsaPf8xsT2Ciu79ddFcH4MuC7yfkl4lIXdSrSDji3cViKGX81EcH6YnUYKW8mLYxs1EF3w9x9yG1PrNZC+AMcuGKyFJTryLhKKnXern782bWeQl3LfrUZ3jBssWf+gCvmllrM2vn7pNqe35NkEWKVUXe92mqu/ct4Zm7Al2Aty23f1VH4A0z6wdMBDoVPLZjfpmI1EW9ioQjeq9Q4kYt1PzUx2rux1zbpz6aIItEUsbzNLr7u8Cqi1dl9jnQ192nmtnDwB/M7G5gE+D7urZsRQT1KhKS0nstaaM27k99tA+ySLGYjrA1s7uAV4AeZjbBzAbV8fDHgM+AscD1wNFx/CgijZ56FQlHec9iUfipz+f8/KnPajTgUx+9gyxSQ3z7SLn7AfXc37ngaweOiWXFIpmhXkXCEe8+yMXi/tRHE2SRYhFOLyMiKaFeRcIRY6/5T322Jbev8gTgHHe/sZaHPwbsQu5Tn9nA4fU9vybIIoVScoJyEYlAvYqEI+Zey/2pjybIIsVScIlLEYlIvYqEI6BewxlpGZnZuWZ2ewXW0zl/ZZeSN0zMbNv8Rwi13T/UzC5YuhEKEMxlMLNMzcpi6jX11KsspktNp4uZzSy4LTSzOQXfH5T0+BoDMzvBzL42sx/yl39ctpbHHVT032N2/h+0jSo95iWzaFf4CWgrOERqtvyiNpt/7H5m9oGZzTCz981sr0qOtXbqNQ3Ua/mV2Otv85dUnmlmI8ysfSXHWrsSek1Bs8mPoALcveWiG7lLD+5esOyOUp6rIVumSTOzVmbWvIzPvxNwGrA9sAawJnDekh7r7ncU/fc4mtzpkt4o1/hKFsjWbWOmZtPTrJl1AG4HTgSWB04G7jSzVZf0+IpTr4lTr6nqdVvgr+SuHLcSMA64q1xjK5neQQ5SUzO7Nf8OyXtmtvjk1Gb2uZmdambvALPMrImZtTez+81sipmNM7PjCh7fz8xG5bf0vjGzy4vWdZCZfWFmU83szII/t6yZXWlmX+VvV9bxTuyGZvZGfrz3AM3q+Nl6Al+Z2XVmtmmDfjt1Gwjc6O7vuft04C/AYSX82UWXf0yeGVQ1iXaTpKnZhiul2Y7Ad+7+uOf8B5hF7pyjyVKvIVGvDVdKr7sB9+UfOzf/2K3NLKxeU9CsJsg/2wO4G2gNPAxcXXT/AcCu+fsXAo8Ab5O7VOH2wB/zW3kAVwFXufvy5F5E7i16ri2BHvk/92czWye//ExgU6A30AvoB5xVPFAzawo8BNxGbgvxPuA3tf1g7v4K0IfcJRXvtNxHpaeYWbui593SzL6r47ZlLatYL/+7WORtoK2ZrVzbmPLrWwPYGri1rsdVXCBbt6JmK9TsKOADM9vDzKott3vFT8A7tY2/otRrKNRr5V5jbQlf96xt/BWld5CD9KK7P+buC8hF0avo/n+4+5fuPgfYGFjF3c9397nu/hm5qykNyD92HtDNzNq4+0x3f7Xouc5z9znu/ja5v+iL1nUQcL67T3b3KeQ+QjlkCWPdFFgGuNLd57n7MOD1un44dx/n7ueS+8fk98DawPtm9qiZrZ5/zIvu3rqO24u1PH1L4PuC7xd93aquMQGHAi+4+7h6HldZgewfJWq2Es3mf7+3AneSmxjfCRzp7rPqGn/FqNdQqNfKvMaOAPYzsw0st9vHnwEHWtQ1/orRPshB+rrg69lAM6u5L9SXBV+vAbQv3PIjd/3vtvn7BwFrAR+a2etmtls962qZ/7o9ML7gvvH5ZcXaAxOLdksYv4TH/UL+z7xP7h+NCeS2TJeL8mfrMJPcvomLLPp6Rj1/7lDglqVcd/wC2boVNbsUIjdrZjsAl5A7IX9TYBvgBjPrvZRjiId6DYV6bbjIvbr708A5wP3A5/nbjPxYkqd3kBulwlC+BMYVbfm1cvddANz9E8+dwHpV4G/AMDOLEshX5P5hWGT1/LJik4AOZjX+Bq1e1xPn973ax8weAT4BNgKOA9Z09w/yj9nKah6NXHzbqpanf4+a7wb0Ar5x92/rGM8W5P4RGlbXuCvOdFR8I6Jm42m2N/C8u49y94Xu/jowEtihrvFXhHptTNRrTK+x7n6Nu3d397bkJspNgDF1jb8iSuk1Bc0mP4IwvQbMsNxBBc3z++X1NLONAczsYDNbxd0XAt/l/8zCCM97F3CWma1iZm3IfTSypHNHvgLMB44zs2XMbG9y+1ItkZltQC7448ntV9XJ3Q9192cLt5Dd/QUvOBp5CbcXalnFrcAgM1vXzFqT26draD0/60Dgfnev713mygtk61ZKomZrKqXZ14GtFr1jbGYbAluhfZClfNRrTZF7NbNm+d+V5XftGEJuf+3pdf9qKkTvIDdu+X2odiP3zso4YCpwA7BC/iH9gffMbCa5gwkG5Perqs8F5A6IeQd4l9ypz35xYnLPHZm6N7mjWKcB+wMP1PG8k4F+7r6Vu98Y96TU3UeQ+wj2WXKn+BlP7iMeACx3xPJBBd83A/YjjbtXAGYW6SbhULO/GE/kZt39f8C55N6lm0HuHam/uvuTcY6podRr46NefzGeUl5jm5E7TmAmuQ2NV4Cz4xzP0ojaaxqaNU/J2bVE0qB6pc7ebPtz6n8gMHvYEaPdvW/9jxSRclCvIuEopVdIvtnkTzQnkirp2HIVkSjUq0g4wupVE2SRIiEFLJJ16lUkHCH1qgmySJGQAhbJOvUqEo6QetUEWaRISAGLZJ16FQlHSL2maoJsTZq7Na3v4muyyIbr1HlaRikwfvznTJ06tf4yLX+TeqnX0qjX0rzxxuip7r5KnQ9Sr5Gp19Ko19I0xl7TNUFu2ople+yX9DCC8dLI4kvZS2222CTagbCGUVWlsx9GoV5Lo15L03wZq/fKZeo1OvVaGvVamsbYa6omyCJpENJHQCJZp15FwhFSr5ogixQJKWCRrFOvIuEIqddw3usWqQQr4VbfU5ndZGaTzWxMwbJLzexDM3vHzB7MXzZ00X2nm9lYM/vIzHaK88cSaZTUq0g4Suk1BfNoTZBFisR4Gcyh5C6JWugpoKe7bwB8DJyeX+e6wABgvfyf+ZeZVcf1M4k0VupVJBxxXmq63Bu1miCLFDDii9fdnwemFS170t3n5799FeiY/3pP4G53/8ndxwFjgX7x/WQijY96FQlHKb2mYaNWE2SRIiXE28bMRhXcBpe4qiOAx/NfdwC+LLhvQn6ZiNRBvYqEI84Jcrk3anWQnkghA6uKvPPTVHePdv644tWYnQnMB+5oyJ8XEdSrSEhK6xXyG7UF3w9x9yEl/PkjgHvyX3cgN2FepN6NWk2QRYpE/GhnaZ7/MGA3YHt39/ziiUCngod1zC8TkTqoV5FwlNhrohu12sVCpEiM+0ct6bn7A6cAe7j77IK7HgYGmNmyZtYF6A68ttQ/jEgjp15FwhHzPsi1reMwchu1By3NRq3eQRYpsOgggliey+wuYFtyHxNNAM4hd8DAssBT+fW86u6/d/f3zOxe4H1yW73HuPuCWAYi0kipV5FwxNlrrev4eaN2myVs1N5pZpcD7YmwUasJskixmPp19wOWsPjGOh5/IXBhPGsXyQj1KhKOGOfH5d6o1QRZpJCFdaUfkUxTryLhiLnXcm/UaoIsUkQvuCLhUK8i4QipV02QRYqUeBoaEUmQehUJR0i9aoIsUiSkLVyRrFOvIuEIqVdNkEUKLO3pZUSkctSrSDhC61UTZJEiIQUsknXqVSQcIfWqCbJIkZACFsk69SoSjpB61QRZpFg4/YqIehUJR0C9aoIsUsigqkpXYBcJgnoVCUdgvWqCLFLAgIA+ARLJNPUqEo7QetUEWaSGsI6yFck29SoSjrB61QRZpEhA/YpknnoVCUdIvWqCLFIkpC1ckaxTryLhCKlXTZBFCllYW7gimaZeRcIRWK+aIIsUMKAqoGvFi2SZehUJR2i9aoIsUiSkgEWyTr2KhCOkXjVBFikU2EdAIpmmXkXCEVivmiCLFMidpzGggkUyTL2KhCO0XsO5pEmZXHvOQYx/5iJG3XfGL+47/pDtmPPm1azcejkAlm/ZjGFXHsnIe05j9LAzOWSPTSs93NQ68rdHsHr7Vdmod8+kh7KUcudpjHKTZKjZeDSOZtVr2pXSa+tWzbnn77/jtXtO54Xb/sS6XdtVerip9uQTI9hgvR6st3Y3Lr3k4qSH0wDRe01Ds5mfIN/2yKvsecw1v1jesW1rtt90Hb6YNG3xsiP325oPP/uaTfa/mJ1+dxUXn/hrlmlSXcnhptYhAw9j+KMjkh5GLMyi3SQZajYejaVZ9ZpupfR6yqCdePujCfTb/yIGnX0bl528TyWHmmoLFizgj8cdw/BHHufNd97nvrvv4oP33096WCWL2msams38BPmlNz5l2vezf7H8kj/9hjOvegh3X7zMgZbLLQvAcs2XZfr3s5m/YGGlhppqW261NSuttFLSw4hFKFu3WaVm49FYmlWv6VZKr2uvuRr/e/1jAD7+/BvWaL8Sq67UqmJjTbPXX3uNrl270WXNNWnatCn77j+ARx8ZnvSwSqZ3kAO327br89Xk73j344k1ll979/9Yu8tqfPbkhYy67wz+dOmwGnFL+MxyR9lGudX/XHaTmU02szEFy1Yys6fM7JP8/6+YX25m9g8zG2tm75hZnzL+mI2Oms0m9Rqm2np99+OJ7LldLwD6rrcGq7dbiQ5tWycxxNT56quJdOzYafH3HTp0ZOLEiXX8ifQppdc0NFvWCbKZ9Tezj/IDOq2c64pL82bLcMoRO3H+v//zi/t+tfk6vPPRBNbc8Uw2GXARV5y2L62Wa5bAKKWcYvz4ZyjQv2jZacAz7t4deCb/PcDOQPf8bTDw7zh+llKE2Cuo2azLaq8QZrN19XrZzU+xQqsWvHr3aRw1YBve/mgCC/SJT6MS8y4WQyljs2WbIJtZNXBNflDrAgeY2brlWl9c1uy4Cmt0WJnX7jmdD/9zHh1Wbc0rd55K25VbccgemzL8v28D8NmXU/l84rf06Nw24RFL3OL6+MfdnwemFS3eE7gl//UtwF4Fy2/1nFeB1mZWsSNUQu0V1GzWZbFXCLfZunqdMetHjjz3djYdcDGDzr6VNiu2ZNzEb5Meciq0b9+BCRO+XPz9xIkT6NChQ4Ijapg4d7Eod7PlPM1bP2Csu38GYGZ35weY6r3K3xv7FWtsf/ri7z/8z3lscdAlfPvdLL78ejrb9uvBS29+yqortWKtzm0ZN3FqgqOVcihh16c2Zjaq4Psh7j6knj/T1t0n5b/+Glg0W+sAfFnwuAn5ZZOojCB7BTWbdRntFQJttq5eV2jZnNk/zmXe/AUc/uvNefGNscyY9WOCo02PvhtvzNixn/D5uHG079CB++65m6G33Zn0sEpWgV2LY2u2nLtY1DaYGsxssJmNMrNRPn9OGYezZLdcdBjP3XISa63RlrEj/sLAvTar9bEXXz+CTXt14fV7z+Cx647lzKuG8+13syo42vQ69OAD2Harzfj4o4/o2rkjQ2+6MekhNYyV9I7UVHfvW3Cr78W2Bs/tDJuWHWKD6BXUbFwaRbPZ7RUiNBtar2uvuRqjh53J2w+ezU5brMufLhlWwZGmW5MmTbjiqqvZfded6L3+Ovxm3/1Yd731kh5WaUroNd9sm0V/f/O3waWsbmmbTfxCIfl/pIYAVLVYteL/+Aw8fWid96+96zmLv5405Xt2P/qXp6sRuPX2u5IeQixyJzIv6yq+MbN27j4p//HO5PzyiUCngsd1zC9LlaR7BTUbl8bQrHqtW2i9jnxnHBvsdX6ZRxSu/jvvQv+dd0l6GA3WgF6nunvfElcTW7PlfAc5iH9ARGoq+yloHgYG5r8eCAwvWH5o/kjbTYHvCz4mqgT1KgHKbK+gZiU4FblQSGzNlvMd5NeB7mbWhVy0A4ADy7g+kVhEOb1MFGZ2F7AtuY+JJgDnABcD95rZIGA8sF/+4Y8BuwBjgdnA4bEMIjr1KkHKaK+gZiVAcfUK5W+2bBNkd59vZn8AngCqgZvc/b1yrU8kFjFewcfdD6jlru2X8FgHjolnzaVTrxKkjPaaH4OalbDEfIW8cjdb1n2Q3f0xcrN2kSDk9pFK/go+SVCvEpos9wpqVsISWq+JH6QnkjYhBSySdepVJBwh9aoJskiRgPoVyTz1KhKOkHrVBFmkSEhbuCJZp15FwhFSr5ogixSK+SACESkj9SoSjsB61QRZpIBhsZ6GRkTKR72KhCO0XjVBFilSFdImrkjGqVeRcITUqybIIkUC6lck89SrSDhC6lUTZJECZmEdRCCSZepVJByh9VrrBNnMlq/rD7r7D/EPRyR5Ae0iVYOalSxSryLhCKnXut5Bfg9wchc/WWTR9w6sXsZxiSQmpC3cImpWMke9ioQjpF5rnSC7e6dKDkQkDYywDiIopGYla9SrSDhC67UqyoPMbICZnZH/uqOZbVTeYYkkp8qi3dJMzUpWqFeRcETtNQ3N1jtBNrOrgf8DDskvmg1cW85BiSTGDIt4Sys1K5mhXkXCUUKvaWg2ylksNnf3Pmb2JoC7TzOzpmUel0hiUtDl0lKzkhnqVSQcIfUaZYI8z8yqyB00gJmtDCws66hEEhLaPlK1ULOSCepVJByh9RplH+RrgPuBVczsPOBF4G9lHZVIgsyi3VJMzUpmqFeRcETtNQ3N1vsOsrvfamajgR3yi/Z19zHlHZZIctKw79PSULOSJepVJBwh9Rr1SnrVwDxyHwFFOvOFSIjMoDoNh88uPTUrjZ56FQlHaL1GOYvFmcBdQHugI3CnmZ1e7oGJJMUi3tJKzUqWqFeRcETtNQ3NRnkH+VBgQ3efDWBmFwJvAheVc2AiSQnpI6BaqFnJDPUqEo6Qeo3yUc4kak6km+SXiTQ6uaNs4zmJuZmdYGbvmdkYM7vLzJqZWRczG2lmY83snjKdzknNSibE2Ssk1qx6lUwopdc0vMbWOkE2syvM7HJgGvCemd1gZtcD7wJTG7pCkVSL6STmZtYBOA7o6+49ye1jOIDc0elXuHs3YDowKL6hq1nJmBgvOlDpZtWrZE6MFwqpRK917WKx6Cja94D/FCx/taErEwlBjJ8ANQGam9k8oAW5d4W2Aw7M338LcC7w75jWp2Ylc2L+xLaSzapXyZyQeq11guzuNzbkCUVCZpR0lG0bMxtV8P0Qdx8C4O4Tzewy4AtgDvAkMBr4zt3n5x8/AegQy8BRs5I9cfUKlW9WvUrWlNgrJPwaW+9BembWFbgQWBdotmi5u6/V0JWKpFkJBxFMdfe+tTzHisCeQBfgO+A+oH8sA6yHmpUsiaPX/PMk0qx6lSwp8SC9RF9joxykNxS4mdzkf2fgXuCeOAchkiYxnYJmB2Ccu09x93nAA8AWQGszW7Rh2hGYGPPwQc1KhsR4yqikmh2KepWMiPE0b2XvNcoEuYW7PwHg7p+6+1nkIhZpdMxy14qPcqvHF8CmZtbCcpvM2wPvA88C++QfMxAYXoYfQ81KJsTYKyTXrHqVTCil1zS8xkY5D/JPZlYFfGpmvyc3G2/V0BWKpF0cBxG4+0gzGwa8Acwnd17TIeQOxrnbzC7ILyvHfohqVjIjroN+EmxWvUpmhNRrlAnyCcBy5E6ncSGwAnBEQ1coknZxncjc3c8Bzila/BnQL5YV1E7NSmbEeeGBhJpVr5IZIfVa7wTZ3Ufmv5wBHBLHSkXSLKAL/SyRmpUsUa8i4Qip11onyGb2IOC13e/ue5dlRCIJMrNST0OTGmpWska9ioQjtF7regf56oqNIm+97h15YMQllV5tsNoeelvSQwjGzM+/jfzYOD8CqrCKNrtO947c++jFlVxl0DoN1okJykG9RtOjawduvf/CSq4yaD1OeiTpITRKIfVa14VCnqnkQETSIsqpXdJIzUoWqVeRcITUa5SD9EQywwhrC1cky9SrSDhC61UTZJEiAe0iJZJ56lUkHCH1GnmCbGbLuvtP5RyMSBqEFHBd1KxkgXoVCUdIvda7O4iZ9TOzd4FP8t/3MrN/ln1kIgkwy30EFOWWVmpWskK9ioSjlF7T0GyU/aX/AewGfAvg7m8D/1fOQYkkqboq2i3F1KxkhnoVCUfUXtPQbJRdLKrcfXzRbH5BmcYjkiiDKNeATzs1K5mgXkXCEVqvUSbIX5pZP8DNrBo4Fvi4vMMSSU4KNlyXlpqVzFCvIuEIqdcoE+SjyH0EtDrwDfB0fplIoxTQBm5t1KxkhnoVCUdIvdY7QXb3ycCACoxFJHFmFtRHQEuiZiUr1KtIOELrtd4JspldzxKuF+/ug8syIpGEBdTvEqlZyRL1KhKOkHqNsovF0wVfNwN+DXxZnuGIJMuAJiGdqHHJ1KxkgnoVCUdovUbZxeKewu/N7DbgxbKNSCRhIW3hLomalTKftx4AABjZSURBVCxRryLhCKnXhlxqugvQNu6BiKSChXWln4jUrDRO6lUkHIH1GmUf5On8vH9UFTANOK2cgxJJkhFQwUugZiVL1KtIOELqtc4JsuXOXN4LmJhftNDdf3EwgUhjkTuRedKjaDg1K1miXkXCEVqvdZ6zOR/qY+6+IH9TuNLoVVm0WxqpWcka9SoSjqi9pqHZKBc1ecvMNiz7SERSwswi3VJMzUpmqFeRcETtNQ3N1rqLhZk1cff5wIbA62b2KTCL3Lvk7u59KjRGkYoxg+qYroVpZq2BG4Ce5PYxPAL4CLgH6Ax8Duzn7tNjWp+alUyJs9fc81WuWfUqWRNar3Xtg/wa0AfYoyFPLBKqGK/0cxUwwt33MbOmQAvgDOAZd7/YzE4jdzDOqTGtT81K5sR8Za5KNqteJXNC6rWuCbIBuPunDXlikRDFdRCBma0AbA0cBuDuc4G5ZrYnsG3+YbcAzxHfBFnNSqbEedBPAs2qV8mU0Hqta4K8ipmdWNud7n55Q1YoknYxbeB2AaYAN5tZL2A0cDzQ1t0n5R/zNfGe71TNSubE+IZUpZtVr5I5JfbaxsxGFXw/xN2H5L8ue691TZCrgZYQ0EnrRJaaURX9r3xd8TYh9/Hpse4+0syuoujcpu7uZhbnUetqVjImtl6h8s2qV8mYknoFmOrufWu5r+y91jVBnuTu5zf0iUVCZJS0hVtXvBOACe4+Mv/9MHLxfmNm7dx9kpm1AyYvzXiLqFnJlBh7hco3q14lU0rstT5l77Wu4wm1VSvZY9CkyiLd6uLuXwNfmlmP/KLtgfeBh4GB+WUDgeHxjl4kQ2LqFRJpVr1KtpTQaxpeY+t6B3n7hj6pSKhi3sI9Frgjf3TtZ8Dh5DZK7zWzQcB4YL/Y1qZmJWNi7hUq26x6lUwJrddaJ8juPq2hTyoSsrhOQ+PubwFL+ki3LC+MalayKM7TRlWyWfUqWRRSr3W9gyySSSm4gI+IRKReRcIRUq+aIIsUMKJdf11EkqdeRcIRWq+aIIsUMlJxDXgRiUC9ioQjsF41QS4waeIETjn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      "text/plain": "<Figure size 720x720 with 18 Axes>"
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = LogisticRegression(C = 0.01)\n",
    "lr.fit(X_train_undersample,y_train_undersample.values.ravel())\n",
    "y_pred_undersample_proba = lr.predict_proba(X_test_undersample.values)\n",
    "\n",
    "thresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "j = 1\n",
    "for i in thresholds:\n",
    "    y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i\n",
    "    # print(y_test_undersample)\n",
    "    plt.subplot(3,3,j)\n",
    "    j+=1\n",
    "\n",
    "    # y_test_predictions_high_recall：是ture和false的bool变量\n",
    "    # 而正好，我们数据里面的class是0，1二分类，所以可以作为测试结果使用\n",
    "    # Compute confusion matrix\n",
    "    cnf_matrix = confusion_matrix(y_test_undersample, y_test_predictions_high_recall)\n",
    "    np.set_printoptions(precision=2)\n",
    " \n",
    "    print(\"Recall metric in the testing dataset: \", cnf_matrix[1, 1] / (cnf_matrix[1, 0] + cnf_matrix[1, 1]))\n",
    " \n",
    "    # Plot non-normalized confusion matrix\n",
    "    class_names = [0, 1]\n",
    "    plot_confusion_matrix(cnf_matrix\n",
    "                          , classes=class_names\n",
    "                          , title='Threshold >= %s' % i)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 过采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": "--------------------------------------\nC parameter:  0.01\n--------------------------------------\nIteration  1  : recall score =  0.8838709677419355\nIteration  2  : recall score =  0.875\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\nIteration  3  : recall score =  0.9644129689056102\nIteration  4  : recall score =  0.9544410371396226\nIteration  5  : recall score =  0.9547927589276882\n\nMean recall score  0.9265035465429714\nresults_table    C_parameter Mean recall score\n0         0.01          0.926504\n1         0.10               NaN\n2         1.00               NaN\n3        10.00               NaN\n4       100.00               NaN\n--------------------------------------\nC parameter:  0.1\n--------------------------------------\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\nIteration  1  : recall score =  0.8903225806451613\nIteration  2  : recall score =  0.875\nIteration  3  : recall score =  0.9441850171517097\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\nIteration  4  : recall score =  0.9545619415042701\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\nIteration  5  : recall score =  0.9564964113386312\n\nMean recall score  0.9241131901279545\nresults_table    C_parameter Mean recall score\n0         0.01          0.926504\n1         0.10          0.924113\n2         1.00               NaN\n3        10.00               NaN\n4       100.00               NaN\n--------------------------------------\nC parameter:  1\n--------------------------------------\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\nIteration  1  : recall score =  0.8838709677419355\nIteration  2  : recall score =  0.875\nIteration  3  : recall score =  0.9441850171517097\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\nIteration  4  : recall score =  0.9548696980688276\nIteration  5  : recall score =  0.9552434024686473\n\nMean recall score  0.922633817086224\nresults_table    C_parameter Mean recall score\n0         0.01          0.926504\n1         0.10          0.924113\n2         1.00          0.922634\n3        10.00               NaN\n4       100.00               NaN\n--------------------------------------\nC parameter:  10\n--------------------------------------\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\nIteration  1  : recall score =  0.8903225806451613\nIteration  2  : recall score =  0.875\nIteration  3  : recall score =  0.9442071483899525\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\nIteration  4  : recall score =  0.9526494542816633\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\nIteration  5  : recall score =  0.9557709851507458\n\nMean recall score  0.9235900336935046\nresults_table    C_parameter Mean recall score\n0         0.01          0.926504\n1         0.10          0.924113\n2         1.00          0.922634\n3        10.00           0.92359\n4       100.00               NaN\n--------------------------------------\nC parameter:  100\n--------------------------------------\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\nIteration  1  : recall score =  0.8903225806451613\nIteration  2  : recall score =  0.875\nIteration  3  : recall score =  0.9442071483899525\n/Users/kongdefeng/Library/Python/3.7/lib/python/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\nIteration  4  : recall score =  0.9548696980688276\nIteration  5  : recall score =  0.9552434024686473\n\nMean recall score  0.9239285659145178\nresults_table    C_parameter Mean recall score\n0         0.01          0.926504\n1         0.10          0.924113\n2         1.00          0.922634\n3        10.00           0.92359\n4       100.00          0.923929\n*********************************************************************************\nBest model to choose from cross validation is with C parameter =  0.9265035465429714\n*********************************************************************************\nRecall metric in the testing dataset:  0.8910891089108911\n"
    },
    {
     "data": {
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      "text/plain": "<Figure size 432x288 with 2 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": "'\\n样本数据不均衡，优先采用过采样\\n'"
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from imblearn.over_sampling import SMOTE  # imblearn不平衡数据模块\n",
    "from sklearn.metrics import confusion_matrix\n",
    "credit_cards = pd.read_csv('creditcard.csv')\n",
    "columns = credit_cards.columns\n",
    "# The labels are in the last column ('Class'). Simply remove it to obtain features columns\n",
    "features_columns = columns.delete(len(columns) - 1)\n",
    "features = credit_cards[features_columns]\n",
    "labels = credit_cards['Class']\n",
    " \n",
    "features_train, features_test, labels_train, labels_test = train_test_split(features,\n",
    "                                                                            labels,\n",
    "                                                                            test_size=0.2,\n",
    "                                                                            random_state=0)\n",
    "\n",
    "oversampler = SMOTE(random_state=0)  # 用SMOTE算法生成数据,random_state=0每次生成的数据都是一样的\n",
    "os_features, os_labels = oversampler.fit_sample(features_train, labels_train)  # 只对训练集生成数据，测试集不用生成数据\n",
    " \n",
    "len(os_labels[os_labels == 1])\n",
    "os_features = pd.DataFrame(os_features)\n",
    "os_labels = pd.DataFrame(os_labels)\n",
    "best_c = printing_Kfold_scores(os_features, os_labels)\n",
    " \n",
    "lr = LogisticRegression(C=best_c)\n",
    "lr.fit(os_features, os_labels.values.ravel())\n",
    "y_pred = lr.predict(features_test.values)\n",
    " \n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(labels_test, y_pred)\n",
    "np.set_printoptions(precision=2)\n",
    " \n",
    "print(\"Recall metric in the testing dataset: \", cnf_matrix[1, 1] / (cnf_matrix[1, 0] + cnf_matrix[1, 1]))\n",
    " \n",
    "# Plot non-normalized confusion matrix\n",
    "class_names = [0, 1]\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix\n",
    "                      , classes=class_names\n",
    "                      , title='Confusion matrix')\n",
    "plt.show()\n",
    " \n",
    "'''\n",
    "样本数据不均衡，优先采用过采样\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}